COVID-19 Detection using Deep Learning on Chest X-rays and CT Scans

📖 Project Overview

This project presents a comparative study of deep learning models — VGG16, ResNet50, InceptionV3, and Xception — for automated COVID-19 diagnosis based on Chest X-ray and CT images.

🎯 Objectives

• Automate COVID-19 detection to assist radiologists.
• Compare the performance of different deep learning architectures.
• Evaluate models based on precision, recall, f1-score, accuracy, and confusion matrices.

🧠 Deep Learning Models Used

• VGG16
• ResNet50
• InceptionV3
• Xception

📂 Dataset

• Chest X-ray images
• Chest CT scan images

⚙️ Methodology

1. Data preprocessing
2. Model training and validation
3. Performance evaluation using classification reports and confusion matrices

📊 Results

EVALUATION AND RESULT

Sample output of the test images

Classification Reports for Chest X-rays: VGG, InceptionV3, ResNet50, Xception

Confusion Matrix for Chest X-rays: VGG, InceptionV3, ResNet50, Xception

Classification Reports for CT Scans: VGG, InceptionV3, ResNet50, Xception

Confusion Matrix for CT Scans: VGG, InceptionV3, ResNet50, Xception

Sample outputs and confusion matrices are available in the /results folder.

🚀 Tech Stack

• Python
• TensorFlow / Keras
• Matplotlib, scikit-learn

🏁 How to Clone

git clone https://github.com/eliashossain001/SARS-CoV-2Detection.git
cd covid19-diagnosis-dl