A Hybrid Deep Learning CNN Model for Enhanced COVID-19 Detection from Computed Tomography (CT) Scan Images

TL;DR

This paper introduces a hybrid deep learning model combining multiple CNN architectures and PCA for improved COVID-19 detection from CT scans, achieving high accuracy and outperforming individual models.

Contribution

A novel hybrid deep learning approach integrating VGG16, DenseNet121, MobileNetV2, PCA, and SVC for enhanced COVID-19 detection from CT images.

Findings

Hybrid model achieved 98.93% accuracy.

Outperformed individual CNN models in precision, recall, and F1 scores.

Demonstrated effectiveness on a dataset of over 2,000 images.

Abstract

Early detection of COVID-19 is crucial for effective treatment and controlling its spread. This study proposes a novel hybrid deep learning model for detecting COVID-19 from CT scan images, designed to assist overburdened medical professionals. Our proposed model leverages the strengths of VGG16, DenseNet121, and MobileNetV2 to extract features, followed by Principal Component Analysis (PCA) for dimensionality reduction, after which the features are stacked and classified using a Support Vector Classifier (SVC). We conducted comparative analysis between the proposed hybrid model and individual pre-trained CNN models, using a dataset of 2,108 training images and 373 test images comprising both COVID-positive and non-COVID images. Our proposed hybrid model achieved an accuracy of 98.93%, outperforming the individual models in terms of precision, recall, F1 scores, and ROC curve performance.