IGF ‐ CNN : An Optimized Deep Learning Model for Covid‐19 Classification

Abstract

ABSTRACT Recent advancements in deep learning and the utilization of pre‐trained convolutional neural network (CNN) architectures have led to enhancements in classification tasks. However, these architectures often entail millions of training parameters, posing challenges for real‐world deployment. In this work, we propose an iterative Gaussian feature extractor with a custom 3‐layer CNN network (IGF‐CNN) coupled with a feedforward artificial neural network (ANN) classifier. The input images undergo pre‐processing before being fed to the proposed IGF‐CNN and then ANN classifies the input into Covid‐19, non‐Covid‐19 and pneumonia classes. The suggested model demands considerably fewer parameters and reduces training time substantially and achieves accuracies of 99.80%, 98.78%, 99.0%, respectively, across three different benchmark datasets. We have also performed cross‐dataset validation and obtained consistently good results, further demonstrating the robustness of the proposed approach. The proposed architecture is accurate and efficient and can be integrated with real‐time systems.