ITDS | SPMS

PNEUMONIA DETECION USING DEEP LEARNING: A FINE-TUNED CNN APPROACH.

Project Description
Supervisor

Pneumonia is a potentially life-threatening infectious disease of the lung(s), and the failure to diagnose it in time increases the risk of severe complications or even death. Diagnoses done by radiologists manually examining chest X-rays are time-intensive and may require a large amount of resources. In order to address these drawbacks, especially in areas where imaging technologies are not available, the current study suggests an automated pneumonia detection model, using Convolutional Neural Networks (CNNs) and transfer learning, which would be trained to differentiate between bacterial and viral types of the disease. To enhance the interpretability, this project applies Grad-CAM (Gradient-weighted Class Activation Mapping), which is a visualisation technique that detects and highlights areas that most significantly contribute to a model’s prediction, creating transparency and increasing user confidence in the diagnostic suggestions. To ensure the maximum accessibility, a cross-platform mobile application has been created, which allows doctors to upload chest X-ray images and acquire immediate predictions of the diagnosis both online and offline. Combined, this work provides an efficient and understandable prototype system that offers medical professionals a convenient and easy-to-use diagnostic tool to identify pneumonia.

Machine Learning: Machine Learning (ML) research in Computer Science and Information Technology focuses on the development of algorithms and models that enable computers to learn from data and improve their performance over time without being explicitly programmed. It is a subset of Artificial Intelligence that uses statistical techniques to give machines the ability to learn patterns, make decisions, and predict outcomes based on data. Supervised learning, a key area of ML research, involves training models on labeled data, where the input-output relationships are predefined. This method is widely used for tasks such as classification (e.g., spam detection) and regression (e.g., predicting house prices). Unsupervised learning, on the other hand, involves finding hidden patterns in data without predefined labels, with clustering and association being typical applications in areas such as customer segmentation and anomaly detection. Reinforcement learning is another area of ML that focuses on teaching agents to make decisions by interacting with their environment and receiving feedback in the form of rewards or penalties. It is often applied in robotics, game playing, and autonomous systems, where continuous learning and adaptation are required.

This study seeks to develop a Convolutional Neural Network (CNN) deep learning model that automatically detects pneumonia accurately in chest X-ray images.

2024/2025

Feb 11, 2026

1. NWAWULU FIDELIA OBIANUJU UEB3220421 2. GOODE KONAMA ANTOINETTE UEB3215421 3. KAMAL DEEN IBRAHIM UEB3213521 4. JANE ABENA KUMAH UEB3222621 5. LAWRENCE AGYEMANG UEB3213321

BSc Information Technology