COVID-19 Detection Using Deep Learning
A deep learning-based medical imaging project designed to classify chest X-ray scans for COVID-19 detection using Convolutional Neural Networks (CNN), TensorFlow, Keras, and an interactive PyQt5 interface.
COVID-19 Detection Using Deep Learning
A deep learning-powered diagnostic support system designed to classify chest X-ray images for COVID-19 detection using Convolutional Neural Networks (CNN). Built with TensorFlow, Keras, and PyQt5 to provide real-time prediction through an interactive graphical interface.
Project Overview
This project focused on developing an AI-powered COVID-19 detection system capable of analyzing chest X-ray images using Convolutional Neural Networks (CNN). The objective was to assist in rapid medical image classification by providing a predictive model that identifies possible COVID-19 infections from radiographic scans. Built using TensorFlow and Keras, the system was integrated with a PyQt5 graphical interface to allow users to upload X-ray images and receive real-time prediction results through an accessible desktop application.
The Challenge
Detecting COVID-19 from chest X-ray images presents several challenges, including limited labeled medical datasets, image quality variations, and the need for accurate classification in a healthcare context. The project required building a reliable deep learning model capable of distinguishing COVID-19 patterns from normal or pneumonia-related lung scans while minimizing false predictions. Another challenge involved creating a user-friendly interface that allows non-technical users to interact with the system and obtain predictions efficiently.
Our Approach
A Convolutional Neural Network (CNN) architecture was developed using TensorFlow and Keras to learn patterns from chest X-ray images. The dataset was preprocessed through resizing, normalization, and augmentation techniques to improve model generalization and performance. After training and validation, the model was integrated into a PyQt5 desktop interface, enabling users to upload X-ray scans and receive real-time predictions. This approach combined deep learning accuracy with accessibility, creating a practical diagnostic support tool.
Impact & Results
The project demonstrated the practical application of deep learning in medical image classification by achieving reliable prediction performance on chest X-ray datasets. It strengthened skills in model training, evaluation, and deployment while showcasing how AI can support faster diagnostic workflows. The addition of a PyQt5 interface transformed the model from a research experiment into an accessible desktop application for real-time COVID-19 detection.
Technology Stack
Model Development Process
Key Features
This COVID-19 detection system combines deep learning with an intuitive desktop interface, enabling users to upload chest X-ray images, run AI-powered classification, and receive fast prediction results through a streamlined medical screening workflow.
Chest X-Ray Image Upload
Allows users to browse and upload chest X-ray images directly through the PyQt desktop interface for AI-based COVID-19 screening.
Deep Learning Classification
Utilizes a Convolutional Neural Network (CNN) model trained on chest X-ray datasets to classify images as COVID-19 or Normal.
Real-Time Prediction Results
Generates fast diagnostic predictions directly within the interface, helping streamline medical image screening and testing workflows.