1. Learning Outcome
This mini R&D project provides you with the opportunity to apply the concepts, knowledge, and skills from this Deep Learning module, to solve a real-world challenge/problem. It also aims to assess how well your learning attainment in terms of the module’s learning outcomes as follows:
Demonstrate knowledge and understanding of the underlying mathematical and algorithm principles of deep learning, including motivation, problem formulation, and factors that have made deep learning successful for various applications.
Demonstrate practical skill in preparing datasets, implementing and evaluating deep learning models for problem solving real-world problems.
Critical appraisal of the merits and shortcomings of model architectures and performances on specific problems.
Research recent scientific deep learning literatures, design, and work on a unique mini research project.
2. Project Synopsis
Pneumonia is an infection by a virus (like Covid-19), bacterial, fungi etc. that causes inflammation in the lungs. It usually manifests as white spots called infiltrates in the lungs or area of increased opacity on chest X-ray (CXR). However, the diagnosis of pneumonia on CXR is complicated because of a number of other conditions in the lungs such as fluid overload (pulmonary edema), bleeding, volume loss (atelectasis or collapse), lung cancer, or post-radiation or surgical changes. Outside of the lungs, fluid in the pleural space (pleural effusion) also appears as increased opacity on CXR.
Pneumonia detection is most common performed by diagnostic imaging study. However, several factors such as positioning of the patient and depth of inspiration can alter the appearance of the CXR, complicating interpretation further. In addition, clinicians are faced with reading high volumes of images every shift.
In this project, you are challenged to design and implement a deep learning algorithm to detect pneumonia from medical images; this is essentially an image classification problem.
For this project, you will be using the Pneumonia CRX images from Kaggle here at https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia. The dataset is organised into 3 folders (train, test, val) and contains subfolders for each image category (Pneumonia/ Normal). There are 5,863 X-Ray images (JPEG) and 2 categories (Pneumonia/Normal). The CRX images (anterior-posterior) were selected from retrospective cohorts of pediatric patients of one to five years old from Guangzhou Women and Children’s Medical Center, Guangzhou. All chest X-ray imaging was performed as part of patients’ routine clinical care.For the analysis of CRX images, all chest radiographs were initially screened for quality control by removing all low quality or unreadable scans. The diagnoses for the images were then graded by two expert physicians before being cleared for training the AI system. In order to account for any grading errors, the evaluation set was also checked by a third expert.
3. Project Tasks & Deliverables
The project essentially involves the following elements:
Dataset: analysis, visualisation, and preparation
Research the challenge and existing solution: Understanding of the challenge and survey of literature and state-of-the-art deep learning models
Solution design/formulation: Highlight your solution in comparison and how it is different than in any existing state-of-the-art solution you referred to etc.
Solution implementation: deep learning network model, network training configuration and strategy
Performance evaluation and discussion
Submit your project work in the form of Jupyter-notebook where you provide the written part and the code implementation part of your project
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