Blood Clot Detection In Lungs Using Deep Learning

Blood Clot Detection In Lungs

Chapter 1: Introduction

Lung blood clot detection is a critical task for clinicians, as early diagnosis and treatment can prevent potentially fatal outcomes. However, the task is difficult, as pulmonary embolism (a blood clot in the lungs) can be difficult to diagnose, particularly in its early stages. The current standard of care for detecting blood clots in the lung is a CT scan, which is expensive and exposes the patient to radiation (Bĕlohlávek, Dytrych & Linhart, 2013).

Recent advances in deep learning have shown promise for automated detection of a wide range of medical conditions (Farhat, Sakr & Kilany, 2020). In this study, we seek to apply deep learning to the task of detecting blood clots in the lungs.

The main components of blood include plasma, red blood cells (RBC), white blood cells (WBC) and platelets. Plasma is a clear colourless fluid that contains water and other substances, and comprises about 55 percent of blood volume. It contains many vital life functions such as transport of oxygen in the body, blood clotting capability and hormonal regulation and, responsible for blood clotting (Batalis & Harley, 2013).

Blood clots are one in all the most important reasons of stroke and coronary heart attack.  Blood can become trapped in sticky blood vessels and block blood flow. This results in a life-threatening situation if blood clots block your brain or heart causing a stroke or heart attack. Blood clotting can start at any age but usually happens in your 20s and 30s. Signs and symptoms include: headache, sudden pain in the chest, arm or leg, numbness or tingling in one or more parts of the body, rapid heartbeat/heart palpitations and shortness of breath (Bĕlohlávek, Dytrych & Linhart, 2013).

There are a variety of diagnostic techniques that physicians can use when a blood clot is suspected. Certain tests use imaging tools such duplex ultrasound, magnetic resonance imaging (MRI), venography, computed tomography scans, magnetic resonance angiography, D-Dimer test, arteriography/angiography, and impedance plethysmography (Batalis & Harley, 2013).

These techniques differ in the methods they use to determine the presence of blood clots and are also specifically designed to detect various medical conditions. They are very expensive, inaccurate and prone to delayed diagnosis, so not all laboratories have them available.

Many researches were studied to detect blood clots in early stages using neural network models, genetic algorithms and Artificial Intelligence (AI). Deep learning detection of blood clots in the lungs works by training a deep neural network to identify blood clots in lungs. The network is trained with a set of images. Once trained, the network can easily identify blood clots in new images, based on their shape and location. Deep learning is a very exciting area of computer science with many applications in the real world. It has already shown power in many application fields, and has great potential to improve the overall performance of machine learning systems (Farhat, Sakr & Kilany, 2020). Therefore, in this research, we will use deep learning methods to predict blood clots in lungs.

1.1 Research Aim and Objectives  

Blood clots in the lung can have devastating consequences. There is no easy or reliable way to detect them, so the best way to avoid problems is to prevent them. This project aims to develop a machine learning model to perform quick and accurate blood clot detection in lungs.

The overall purpose of this project is to apply deep learning in order to detect blood clots in lungs. This can save lives of people who have pulmonary embolism, a potentially fatal condition that involves clots in the arteries of the lung.

The following objectives will be accomplish to achieve the general objective of
the study.

  • To conduct comprehensive systematic literature review so as to identify methods, algorithms and approaches used in this study.
  • To label the data by experts.
  • To prepare training and test dataset.
  • To identify suitable deep learning algorithms.
  • To develop an optimal model to detect blood clot in lungs.
  • To test and evaluate the performance of the proposed model.

1.2 Scope and Limitations

This study will only focus on developing a deep learning model to detect blood clots in the lungs. Deep learning algorithms are powerful tools that can be used to detect and diagnose a wide range of conditions, including blood clots. These algorithms can detect blood clots more accurately and quickly than traditional methods, and they can be used to detect and diagnose conditions even in early stages.

Additionally, deep learning algorithms are more cost-effective and require less manual labor than traditional methods. However, it has also certain limitations to using them to detect blood clots. For example, deep learning algorithms require large amounts of data for training and may not be able to detect rare conditions or conditions in early stages. Additionally, the accuracy of deep learning algorithms can vary depending on the data used for training and the quality of the images used for analysis.

Chapter 2: Literature Review

2.1 Background Overview

Pulmonary embolism (PE) refers to blood clots in the pulmonary arterial system of the lungs, which typically originate in the deep veins of the legs and move to the lung’s blood vessels, where they become trapped. PE results in decreased blood flow and oxygen levels to the lungs as well as to other organs in the body. PE is linked to substantial morbidity and mortality (Bĕlohlávek, Dytrych & Linhart, 2013).

Multiple risk factors, including immobilisation, recent surgery, a history of clotting disorders, malignancy, obesity, pregnancy, cigarette smoking, certain medications such as birth control pills, and medical conditions such as heart disease, predispose patients to the development of pulmonary embolism (PE) (Batalis & Harley, 2013). Early diagnosis and treatment can significantly lower the chance of mortality. Consequently, correct diagnosis is essential for these patients (Brevik, 2019).

In this aspect, Deep Learning can assist in predicting the existence of PE by emphasising regions of interest with varied degrees of certainty, resulting in a quicker and more precise diagnosis for the patient. This instrument can assist in identifying life-threatening PEs, particularly those that are acute and central. It is crucial to detect PEs early because they are associated with a greater death rate. This method can also assist in ruling out PE and various subtypes of existent PE, allowing radiologists to prioritise tests and triage patient management (Farhat, Sakr & Kilany, 2020).

2.2 Overview of Deep Learning

AI is a general word that covers a wide range of techniques. Based on neural networks, deep learning is a branch of artificial intelligence. These artificial networks are made up of many interconnected layers of neurons. In essence, each neuron consists of a single linear regression unit. Each neuron receives its input from the neurons in the layer above. “Weights” refers to the connections between the neurons. comparing and contrasting synthetic and biological neural networks. There are numerous interconnected layers that make up neural networks. The network receives data, and an output is created. An error can be calculated by comparing the network’s output to the desired true label. The method optimizes connections between the layers based on the error. “Weights” refers to the connections between the neurons. A tuned network is eventually achieved.

Input data is supplied into the network during training, and the result is computed. Error estimation is possible because of the discrepancy between the estimated label, which is the network output, and the genuine label. The algorithm can adjust the weights of the network to optimize it by estimating the error of the model output. Backpropagation is the name of this network optimization procedure. Important network connections are strengthened and irrelevant connections are blocked by adjusting the weights. In this method, the inaccuracy of the network is reduced and the discrepancy between the network outputs and the genuine labels is minimized.

2.3 Neural Networks with Convolutions

The cornerstone deep learning networks for image processing are convolutional neural networks (CNN). According to (Müller & Kramer, 2021) CNNs were created specifically to process images. There are numerous filters in each CNN layer (Guo et al., 2018). Similar to the weights of general neural networks, each filter is a tiny matrix of weights. Pixels in the image are subjected to the filters repeatedly. The filters notice recurring patterns since they are applied to the entire image. CNNs are therefore perfect for analyzing photos because they are made up of recurring patterns. Lines, circles, and other basic geometric patterns are recognized by CNN’s shallow layers as low-level patterns. A high-level comprehension of the image, such as context (i.e., “image with PE” vs. “image without PE”), is gained via the deeper layers. Medical image analysis has seen a significant revolution thanks to CNN in recent years (Shen et al., 2017). Convolutional neural network architecture (CNN). CNNs are networks created specifically for image processing. Each CNN layer is made up of many tiny filters. The image pixels are subjected to a series of applications of a tiny matrix of weights known as a filter. Recurring patterns are identified by applying the filter to the entire image. Since images are made up of recurring patterns, CNNs are excellent for image analysis. Low-level patterns are recognized by CNN’s shallow layers. Higher-level knowledge of the image is gained via the deeper layers.

The nerve systems seen in biological species serve as an inspiration for neural networks. It is made up of neurons, which act as data processing units, coupled by movable connection weights. The layers of neurons consist of an input layer, one or more hidden layers, and an output layer. There is no set rule that specifies how many concealed layers there should be. The network’s connections between its components serve as a major foundation for determining the function. Each neuron in the input layer is assigned to a certain input parameter. The neural network learns by using a back-propagation method that computes a prediction error by comparing the neural network’s simulated output values to the actual values. The network then back propagates the error through itself, adjusting the weights that are most responsible for the error in an effort to reduce the prediction error. The network is trained or taught by having cycles of data patterns (also known as epochs or iterations) introduced to it. The propensity of the network to memorize the training data following a prolonged learning period is one issue with neural network training. It is more challenging for the network to generalize to a data set that it was not exposed to during training if it overlearns the training data. As a result, it’s standard procedure to split the data set into a learning data set and a validation data set. The learning data set is used to train the network, while the validation data set is used to test network performance.

2.4 Deep Learning for Medical Diagnoses

Deep learning algorithms have improved the speed and accuracy of medical diagnoses in clinics. For the purpose of diagnosing diseases in a healthcare setting, several researchers are creating computer-aided diagnosis systems. The diagnosis of medical signals using deep learning techniques is sole purpose of the research. However, electrocardiogram (ECG) and electroencephalogram (EEG) diagnosis typically employ machine learning techniques (Thiagarajan et al., 2020). To reduce artifact noise and recover the signals’ characteristics, these signals must first undergo pre-processing. The support vector machine, fuzzy neural network, K-nearest neighbor and Adaboost are a few examples of the machine learning techniques that may be used. In fact, machine learning techniques enable faster data processing. However, the qualities of retrieved features and signal processing techniques have a direct impact on machine learning performance.

Recent years have seen the development of deep learning algorithms that can analyze time series signals from devices like gyroscopes and accelerometers for body activity detection, ECGs for the classification of heartbeat arrhythmias, and EEGs for the detection of brain waves. Long short-term memory (LSTM), a sequential deep learning model, and a variant of the recurrent neural network (RNN) have the best performances among the several deep learning techniques for processing temporal information (Liu et al., 2020). Some studies convert the signal’s pattern into an image and perform classification using a two-dimensional convolutional neural network (2D CNN), such as when categorizing arrhythmia in ECG signals and the photoplethysmography-measured pulse waveform signal quality (PPG). A meta-analysis can assess the functional variation of some organs by looking at the transitory response of physiological indicators. (Han et al., 2016) assessed the flexibility of the radial artery using the spectral harmonic energy of the pulse signal ratio. They discovered that palpitation patients had an excessive drop in the spectral energy in the fourth to sixth harmonics. Numerous studies have revealed a strong correlation between the heart rate recovery following exercise and the risk of death. It is an intriguing challenge that calls for new techniques to determine how to apply deep learning algorithms to assess or predict the risks of specific diseases.

2.5 Visual Computing in Healthcare

An area of engineering called computer vision is focused on employing algorithms like CNN to analyze images. Segmentation, detection, and classification are the three primary computer vision tasks. The labeling of a whole image is known as classification. The localization of a specific object within the image is called detection. Segmentation is the process of pixel-by-pixel delineating an individual object’s borders in an image. (Ma et al., 2022) discussed in their study that by the examination of CTPA with PE, these three tasks can be comprehended. It is possible to categorize the entire scan as either pathologic (with PE) or normal (no PE). Additionally, we can find specific emboli. Finally, we can divide the emboli’s pixel-wise bounds.

The conventional test for the diagnosis of DVT is ultrasonography, which is also one of the most accurate methods. More recently, ML approaches have also been used to diagnose DVT. The percentage is not always the same and is initially not very high, but as a sonographer acquires experience and training throughout their career, the tests become more accurate. Nevertheless, there is research that shows promise in combining magnetic resonance imaging (MRI) and deep learning (DL). Artificial neural network analysis has recently been found to improve risk stratification for patients who present with suspected DVT. The authors (Leonardi et al., 2006) demonstrated that a NN is capable of diagnosing DVT without the use of ultrasound and has a low false negative rate. For the quick, unobtrusive, and accurate diagnosis of DVT, a novel ML model was created. This is based on pattern recognition methods that facilitate quick diagnosis as well as properly trained machine learning models that facilitate decision-making and confirm whether a person has this condition or not.

In recent years, the discipline of data science has led the way in the development of both hardware and software for the application of Artificial Neural Networks (ANNs) in clinical diagnosis, which can be useful for the detection of DVT and other disorders generally. ML models include Support Vector Machines (SVM), Decision Trees, and Neural Networks, for instance (Suthaharan, 2016). Alternative DVT diagnosis methods currently exist, some of which use AI (Goodacre et al., 2005). The Random Forest (RF) model, for instance, has superior specificity and sensitivity when compared to the Padua model when used to assess the risk of venous thromboembolism (VTE) in China. By utilizing efficient machine learning to identify the key risk factors of VTE and using patient data from the medical ward at King Chulalongkorn Memorial Hospital in Thailand, the authors developed an automatic diagnosis model. Other initiatives are being made to use ML techniques to predict VTE in young and middle-aged inpatients (Liu et al., 2021); for instance, VTE risk classifiers are being developed utilizing models based on multi-kernel learning and random optimization. These systems’ disadvantages include cost, size, weight, and modest energy usage. On the other side, edge computing can speed up data processing, assure data security (since it is closer to end users, it provides better privacy), cut reaction times, make designs simple, and cost little. It provides good application value and enhances overall data quality and usage efficiency as a result of efficient handling. Benefits include low latency, high dependability, superior energy conservation, and high real-time processing.

2.6 Pulmonary Embolism Diagnosis using Deep Learning

Blood coagulates, causing blood clots to develop. A thrombus is a blood clot that develops in a blood artery or the heart and remains there. An embolus/embolism is a blood clot that exits the site of its creation (disorder). A blood vessel may become attached to by thrombi or emboli, which may then fully or partially stop the flow of blood through the channel. Ischemia is a blockage that hinders correct oxygen intake and regular blood flow. The tissues in that location suffer injury or death (infarction or necrosis) if prompt treatment is not given. Deep venous thrombosis is one of the most widespread types of blood clot problems.

A range of diseases that may appear in various body parts can be caused by thrombi. A number of risk factors, such as prolonged sitting, blood coagulation disorders, inactivity brought on by recent injury or surgery pregnancy, or recent birth complications, oral contraceptive use, estrogen therapeutic, cancer, obesity, stroke smoking, and central venous catheters, increase the likelihood of getting one of these disorders as a result of a blood clot (used for injection of medications or for imaging). Blood clots can result in a wide variety of various ailments, and each disorder has its own unique set of symptoms.

When a blood clot is detected, doctors have access to a wide range of diagnostic methods. These comprise, but are not restricted to, venography, computed tomography scans, duplex ultrasound, magnetic resonance imaging, magnetic resonance angiography, D-Dimer test, arteriography/angiography, and impedance plethysmography. These procedures differ in the approach they take to determining whether a blood clot is present, and they are furthermore made to identify particular diseases.

2.7 Visual Computing for Pulmonary Embolism

Due to free-text narration, medical imaging reports contain significant diagnostic information that is inaccessible to machine analysis. If this unstructured free text was translated into a computer-manageable format, it may be utilised in a number of circumstances needing automatic information extraction. In a recent study, Banerjee and colleagues investigated a deep learning strategy for classifying free-text radiology reports associated with the PE diagnosis—PE present/absent; PE acute/chronic; PE central/subsegmental alone (Chen et al., 2018). They demonstrated great fidelity compared to the most advanced rule-based system and found it to be applicable across institutions for the clinically relevant categories associated with PE diagnosis (Tajbakhsh, Gotway & Liang, 2015).

In another work by the same group (Banerjee et al., 2018), the performance of a deep learning CNN model was compared to that of a classical natural language processing model for extracting PE findings from two institutions’ thoracic CT reports. In contrast-enhanced chest CT reports, the CNN model achieved an accuracy of 99.9% and an area under the curve of 0.97 for determining the presence of PE. As highlighted by these authors, these techniques may make the valuable diagnostic information in radiology text accessible on a large scale for use in models that evaluate imaging utilisation, as part of clinical decision support models, to predict outcomes, and as a valuable tool for evaluating ordering provider imaging yield rates.

Classification of lung vessels as arterial or venous may be of great use to physicians in accurately diagnosing pulmonary illnesses that may affect either the arterial or venous trees. Recent studies have indicated, for instance, that A/V classification allows for a more accurate assessment of pulmonary emboli, whereas arterial tree modifications have been linked to the development of chronic thromboembolic pulmonary hypertension (CTEPH). Additionally, intraparenchymal pulmonary artery alterations have been linked to right ventricular dysfunction. A fundamental method for separating the 2 vascular trunks involves the manual analysis of individual CT slices to trace the blood vessels back to their origin in search of characteristics that distinguish arteries and veins. However, CT picture characteristics such as a large number of slices, scan resolution, and the partial volume effect, as well as the extreme complexity and density of the vascular tree, make this manual separation a time-consuming and error-prone process (Ma et al., 2022). Therefore, the capacity to semi-automatically segment vascular structures on CT images may be essential for enhancing the physician’s ability to diagnose pathological situations (Nardelli et al., 2018).


Banerjee, I., Chen, M. C., Lungren, M. P., & Rubin, D. L. (2018). Radiology report annotation using intelligent word embeddings: Applied to multi-institutional chest CT cohort. Journal of biomedical informatics77, 11-20.

Batalis, N. I., & Harley, R. A. (2013). Pulmonary Embolic Disorders. Academic Forensic Pathology3(4), 420-434.

Bĕlohlávek, J., Dytrych, V., & Linhart, A. (2013). Pulmonary embolism, part I: Epidemiology, risk factors and risk stratification, pathophysiology, clinical presentation, diagnosis and nonthrombotic pulmonary embolism. Experimental & Clinical Cardiology18(2), 129.

Brevik, K. A. (2019). Management of pulmonary embolism patients in the emergency department setting (Doctoral dissertation, University of Zagreb. School of Medicine. Department of Internal Medicine).

Chen, M. C., Ball, R. L., Yang, L., Moradzadeh, N., Chapman, B. E., Larson, D. B., … & Lungren, M. P. (2018). Deep learning to classify radiology free-text reports. Radiology286(3), 845-852.

Farhat, H., Sakr, G. E., & Kilany, R. (2020). Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19. Machine vision and applications31(6), 1-42.

Goodacre, S., Sampson, F., Thomas, S., van Beek, E., & Sutton, A. (2005). Systematic review and meta-analysis of the diagnostic accuracy of ultrasonography for deep vein thrombosis. BMC medical imaging5(1), 1-13.

Guo, Y., Liu, Y., Georgiou, T., & Lew, M. S. (2018). A review of semantic segmentation using deep neural networks. International journal of multimedia information retrieval7(2), 87-93.

Han, X., Chen, X., Tang, X., Chen, Y. L., Liu, J. H., & Shen, Q. D. (2016). Flexible polymer transducers for dynamic recognizing physiological signals. Advanced Functional Materials26(21), 3640-3648.

Leonardi, M. J., McGory, M. L., & Ko, C. Y. (2006). The rate of bleeding complications after pharmacologic deep venous thrombosis prophylaxis: a systematic review of 33 randomized controlled trials. Archives of Surgery141(8), 790-799.

Liu, H., Yuan, H., Wang, Y., Huang, W., Xue, H., & Zhang, X. (2021). Prediction of venous thromboembolism with machine learning techniques in young-middle-aged inpatients. Scientific Reports11(1), 1-12.

Liu, W., Liu, M., Guo, X., Zhang, P., Zhang, L., Zhang, R., … & Xie, S. (2020). Evaluation of acute pulmonary embolism and clot burden on CTPA with deep learning. European radiology30(6), 3567-3575.

Ma, X., Ferguson, E. C., Jiang, X., Savitz, S. I., & Shams, S. (2022). A multitask deep learning approach for pulmonary embolism detection and identification. Scientific Reports12(1), 1-11.

Müller, D., & Kramer, F. (2021). MIScnn: a framework for medical image segmentation with convolutional neural networks and deep learning. BMC medical imaging21(1), 1-11.

Nardelli, P., Jimenez-Carretero, D., Bermejo-Pelaez, D., Washko, G. R., Rahaghi, F. N., Ledesma-Carbayo, M. J., & Estépar, R. S. J. (2018). Pulmonary artery–vein classification in CT images using deep learning. IEEE transactions on medical imaging37(11), 2428-2440.

Shen, D., Wu, G., & Suk, H. I. (2017). Deep learning in medical image analysis. Annual review of biomedical engineering19, 221.

Suthaharan, S. (2016). Machine learning models and algorithms for big data classification. Integr. Ser. Inf. Syst36, 1-12.

Thiagarajan, J. J., Rajan, D., Katoch, S., & Spanias, A. (2020). DDxNet: a deep learning model for automatic interpretation of electronic health records, electrocardiograms and electroencephalograms. Scientific reports10(1), 1-11.

Order Now

Get expert help for Blood Clot Detection In Lungs and many more. 24X7 help, plag free solution. Order online now!

Universal Assignment (April 14, 2024) Blood Clot Detection In Lungs Using Deep Learning. Retrieved from
"Blood Clot Detection In Lungs Using Deep Learning." Universal Assignment - April 14, 2024,
Universal Assignment January 2, 2023 Blood Clot Detection In Lungs Using Deep Learning., viewed April 14, 2024,<>
Universal Assignment - Blood Clot Detection In Lungs Using Deep Learning. [Internet]. [Accessed April 14, 2024]. Available from:
"Blood Clot Detection In Lungs Using Deep Learning." Universal Assignment - Accessed April 14, 2024.
"Blood Clot Detection In Lungs Using Deep Learning." Universal Assignment [Online]. Available: [Accessed: April 14, 2024]

Please note along with our service, we will provide you with the following deliverables:

Please do not hesitate to put forward any queries regarding the service provision.

We look forward to having you on board with us.


Get 90%* Discount on Assignment Help

Most Frequent Questions & Answers

Universal Assignment Services is the best place to get help in your all kind of assignment help. We have 172+ experts available, who can help you to get HD+ grades. We also provide Free Plag report, Free Revisions,Best Price in the industry guaranteed.

We provide all kinds of assignmednt help, Report writing, Essay Writing, Dissertations, Thesis writing, Research Proposal, Research Report, Home work help, Question Answers help, Case studies, mathematical and Statistical tasks, Website development, Android application, Resume/CV writing, SOP(Statement of Purpose) Writing, Blog/Article, Poster making and so on.

We are available round the clock, 24X7, 365 days. You can appach us to our Whatsapp number +1 (613)778 8542 or email to . We provide Free revision policy, if you need and revisions to be done on the task, we will do the same for you as soon as possible.

We provide services mainly to all major institutes and Universities in Australia, Canada, China, Malaysia, India, South Africa, New Zealand, Singapore, the United Arab Emirates, the United Kingdom, and the United States.

We provide lucrative discounts from 28% to 70% as per the wordcount, Technicality, Deadline and the number of your previous assignments done with us.

After your assignment request our team will check and update you the best suitable service for you alongwith the charges for the task. After confirmation and payment team will start the work and provide the task as per the deadline.

Yes, we will provide Plagirism free task and a free turnitin report along with the task without any extra cost.

No, if the main requirement is same, you don’t have to pay any additional amount. But it there is a additional requirement, then you have to pay the balance amount in order to get the revised solution.

The Fees are as minimum as $10 per page(1 page=250 words) and in case of a big task, we provide huge discounts.

We accept all the major Credit and Debit Cards for the payment. We do accept Paypal also.

Popular Assignments

Assignment: Implement five dangerous software errors

Due: Monday, 6 May 2024, 3:00 PM The requirements for assessment 1: Too many developers are prioritising functionality and performance over security. Either that, or they just don’t come from a security background, so they don’t have security in mind when they are developing the application, therefore leaving the business

Read More »


Business School                                                                 London campus Session 2023-24                                                                   Trimester 2 Module Code: LNDN08003 DATA ANALYTICS FINAL PROJECT Due Date: 12th APRIL 2024 Answer ALL questions. LNDN08003–Data Analytics Group Empirical Research Project Question 2-The project (2500 maximum word limit) The datasets for this assignment should be downloaded from the World Development Indicators (WDI)

Read More »

Microprocessor Based Systems: Embedded Burglar Alarm System

ASSIGNMENT BRIEF 2023/24 Microprocessor Based Systems   Embedded Burglar Alarm System Learning Outcomes This assignment achieves the following learning outcomes:   LO 2 -Use software for developing embedded systems in ‘C’ and testing microcontroller systems including the use of design tools such as Integrated Development Environments and In Circuit Debugger.

Read More »

Imagine you are an IT professional and your manager asked you to give a presentation about various financial tools used to help with decisions for investing in IT and/or security

Part 1, scenario: Imagine you are an IT professional and your manager asked you to give a presentation about various financial tools used to help with decisions for investing in IT and/or security. The presentation will be given to entry-level IT and security employees to understand financial investing. To simulate

Read More »

DX5600 Digital Artefact and Research Report


Read More »

Bsc Public Health and Health Promotion (Top up) LSC LONDON

Health and Work Assignment Brief.                 Assessment brief: A case study of 4,000 words (weighted at 100%) Students will present a series of complementary pieces of written work that:   a) analyse the key workplace issues; b) evaluate current or proposed strategies for managing them from a public health/health promotion perspective

Read More »

6HW109 Environmental Management and Sustainable Health

ASSESSMENT BRIEF MODULE CODE: 6HW109 MODULE TITLE: Environmental Management and Sustainable Health MODULE LEADER: XXXXXXXXX ACADEMIC YEAR: 2022-23 1        Demonstrate a critical awareness of the concept of Environmental Management linked to Health 2        Critically analyse climate change and health public policies. 3        Demonstrate a critical awareness of the concept of

Read More »


PROFESSIONAL SECURE NETWORKS– Case Study Assessment Information Module Title: PROFESSIONAL SECURE NETWORKS   Module Code: COCS71196 Submission Deadline: 10th May 2024 by 3:30pm Instructions to candidates This assignment is one of two parts of the formal assessment for COCS71196 and is therefore compulsory. The assignment is weighted at 50% of

Read More »


CYBERCRIME FORENSIC ANALYSIS – COCS71193 Assignment Specification Weighted at 100% of the module mark. Learning Outcomes being assessed by this portfolio. Submission Deadline: Monday 6th May 2024, 1600Hrs. Requirements & Marking Scheme General Guidelines: This is an individual assessment comprised of four parts and is weighted at 100% of the

Read More »

Social Media Campaigns (SMC) Spring 2024 – Winter 2024

Unit: Dynamic Websites Assignment title: Social Media Campaigns (SMC) Spring 2024 – Winter 2024 Students must not use templates that they have not designed or created in this module assessment. This includes website building applications, free HTML5 website templates, or any software that is available to them to help with

Read More »


ASSIGNMENT/ TUGASAN _________________________________________________________________________ ABCJ3103 NEWS WRITING AND REPORTING PENULISAN DAN PELAPORAN BERITA JANUARY 2024 SEMESTER SPECIFIC INSTRUCTION / ARAHAN KHUSUS Jawab dalam bahasa Melayu atau bahasa Inggeris. Jumlah patah perkataan: 2500 – 3000 patah perkataan tidak termasuk rujukan. Hantar tugasan SEKALI sahaja dalam PELBAGAIfail. Tugasan ini dihantar secara ONLINE. Tarikh

Read More »


ASSIGNMENT/ TUGASAN _________________________________________________________________________ ABCM2103 INFORMATION TECHNOLOGY, MEDIA AND SOCIETY TEKNOLOGI MAKLUMAT, MEDIA DAN MASYARAKAT JANUARY 2021 SPECIFIC INSTRUCTION / ARAHAN KHUSUS Jawab dalam Bahasa Melayu atau Bahasa Inggeris. Jumlah patah perkataan : 2500 – 3000 patah perkataan tidak termasuk rujukan. Hantar tugasan SEKALI sahaja dalam SATU fail. Tugasan ini dihantar

Read More »


ASSIGNMENT/ TUGASAN _________________________________________________________________________ ABCR3203 COMMUNICATION LAW UNDANG-UNDANG KOMUNIKASI JANUARY 2024 SEMESTER SPECIFIC INSTRUCTION / ARAHAN KHUSUS Jawab dalam Bahasa Melayu atau Bahasa Inggeris. Jumlah patah perkataan : 2500 – 3000 patah perkataan tidak termasuk rujukan. Hantar tugasan SEKALI sahaja dalam SATU fail. Tugasan ini dihantar secara ONLINE. Tarikh penghantaran        :

Read More »


POSTGRADUATE DIPLOMA IN BUSINESS MANAGEMENT ORGANISATIONAL STRATEGY PLANNING AND MANAGEMENT ASSIGNMENT NOTE: At postgraduate level, you are expected to substantiate your answers with evidence from independent research. INTRODUCTION TO THE ASSIGNMENT • This assignment consists of FOUR compulsory questions. Please answer all of them. • When you answer, preferably use

Read More »

Solution: Scenario 1, Mirror therapy in patients post stroke

Title: Scenario 1, Mirror therapy in patients post stroke Part 1 : Summary Ramachandran and colleagues developed mirror therapy to treat amputees’ agony from phantom limbs. Patients were able to feel their amputated limb without experiencing any pain by presenting them a mirror image of their healthy arm. Since then,

Read More »

Solution: Exploring the Dominance of Silence

Slide 1: Title – Exploring the Dominance of Silence The title, “Exploring the Dominance of Silence,” sets the stage for a deep dive into the portrayal of silence in Philip K. Dick’s “Do Androids Dream of Electric Sheep?” Our presentation will dissect the literary techniques used by the author to

Read More »

Solution: Assessment: Critical Reflection S2 2023

The policies that hampered the cultural survival of Indigenous groups have a major effect on their health (Coffin, 2007). Cultural isolation can cause an identity crisis and a sense of loss, which can exacerbate mental health problems. Indigenous people have greater rates of chronic illness and impairment due to historical

Read More »

Solution: The Market – Product and Competition Analysis

Section 1: The Market – Product and Competition Analysis Industry and Competition Analysis: The baking mix market is very competitive, but My Better Batch is entering it anyhow. The prepackaged baking mixes sold in this market allow busy people to have bakery-quality products on the table quickly without sacrificing quality

Read More »

Solution: PDCA model for Riot

Student Name: Student ID: University Name: Date: Learning Outcome 1: Engage actively in recognizing a new product/service for Riot and detect the vital tasks required for its effective growth. In this comprehensive learning outcome, Riot’s progress towards innovation superiority is characterized by a deliberate scheme that draws on components from

Read More »

Solution: EDEN 100 – ASSIGNMENT 1

Part 1: Reflections on the Register Variables Use the questions in Column 1 and analyse the sample oral interactions provided under the assessment tile. The transcript for Viv’s conversation is provided on pages 4-5. Probe Questions  Link to readings and theory Interaction 1 Interaction 2 PART 1 – ANALYSING THE

Read More »

Solution: TCP/IP Questions

Table of Contents Question 1. 1 1. IPSec datagram protocol 1 2. Source and destination IP addresses in original IP datagram.. 1 3. Source and destination IP addresses in new IP header 2 4. Protocol number in the protocol field of the new IP header 2 5. Information and Bob.

Read More »

Solution: Fundamentals of Employment Assistance Program and Counselling

ASSESSMENT 3 Subject: Fundamentals of Employment Assistance Program and Counselling Case study Question 1 a)     Major Issues for Theo that could be addressed in counselling: b)    Issues to Address First in Short-Term Counselling:             The cognitive processes of memory, focus, and decision-making are all impacted by insufficient sleep. Such cognitive

Read More »


Written Policy Recommendation Name: Student Number: Email: Date: Introduction: The early years of a child’s life are important for their holistic development, making early childhood education a foundation for their future accomplishments. Nevertheless, guaranteeing equality and inclusion in early childhood education stays a major problem in our society. This policy

Read More »

Solution: Report Health Issue

Table of Contents Executive Summary                                                                                                   3 Introduction                                                                                                                5 Examination of the Chosen Health Issue in the Context of Lambeth                        5 Application of Health Inequality Framework and Analysis of Determinants: Psychotropic Drug Use in Lambeth                                                                           6 Exploration and Discussion of Strategies to Manage Psychotropic Drug Use in Lambeth                                                                                                                        7 Conclusion                                                                                                                  8

Read More »

Solution: Section III: Marketing

Section III: Marketing Channels for Advertising: Understanding Who Makes Baking Product Purchase Decisions is Crucial for My Better Batch’s Business Success (Sampson et al, 2017). Home bakers may make up a disproportionate share of the decision-makers in the UK. As a result, My Better Batch has to target people, especially

Read More »

Can't Find Your Assignment?

Open chat
Free Assistance
Universal Assignment
Hello 👋
How can we help you?