CNN_COVID19_detection

This repository contains the implementation of a Convolutional Neural Network (CNN) model for detecting COVID-19 from chest X-ray images using the EfficientNetB1 pre-trained model.

Table of Contents

• Introduction
• Dataset
• Model Architecture
• Training
• Evaluation
• Results

Introduction

The COVID-19 pandemic has highlighted the need for rapid and accurate diagnostic tools. This project leverages deep learning, specifically a Convolutional Neural Network (CNN), to identify COVID-19 infection from chest X-ray images. The model is built on the EfficientNetB1 architecture, which is known for its efficiency and performance on image classification tasks.

Dataset

The dataset used for training and evaluation includes chest X-ray images labeled as COVID-19 positive, pneumonia, and normal. The dataset can be downloaded from Kaggle's COVID-19 Radiography Database.

Installation

Clone this repository to your local machine:

git clone https://github.com/aayushkanjani/CNN_COVID19_detection.git
cd CNN_COVID19_detection

Model Architecture

The model uses the EfficientNetB1 pre-trained model as the base. The architecture includes:

EfficientNetB1 base model (pre-trained on ImageNet)
using max pooling
Fully connected layer with dropout for regularization
Output layer with softmax activation for classification

Training

The model is trained using the following settings:

Optimizer: Adamax
Loss function: Categorical Crossentropy
Metrics: Accuracy
Number of epochs: 25 (can be adjusted)
Batch size: 32 (can be adjusted)

Evaluation

The model is evaluated on a separate test set. Evaluation metrics include accuracy and confusion matrix.

Results

The model achieved an accuracy score of 95.37% on the test data.