CTEC3451 - Computer Networks and Security Module Assignment

Assignment Task

Introduction

Lung blood clot detection is critical 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 challenging 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.

Recent advances in deep learning have shown promise for the automated detection of a wide range of medical conditions. In this proposal, I seek to apply deep learning to detect lung blood clots.

Background

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 per cent of blood volume. It contains many vital life functions such as the transport of oxygen in the body, blood clotting capability and hormonal regulation and is responsible for blood clotting.

Blood clots are one of the most important reasons for 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 body parts, rapid heartbeat/heart palpitations and shortness of breath.

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

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

Many types of research were studied to detect blood clots in early stages using neural network models [1], [2], [3], genetic algorithms [4] and Artificial intelligence [5].

In this paper, we will use deep learning methods to predict lung blood clots. Deep learning is a fascinating area of computer science with many applications in the real world. It has already shown power in many application fields and has excellent potential to improve the overall performance of machine learning systems.

Deep learning detection of blood clots in the lungs works by training a deep neural network to identify blood clots in the lungs. The network is trained with a set of images. Once trained, the network can quickly identify blood clots in new images based on their shape and location.

Objectives

The general objective 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.

Specific Objective

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

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

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