BRIDGING DEPTH AND BREADTH: A WIDE–DEEP CUSTOM NEURAL NETWORK FOR ENHANCED COVID-19 DETECTION FROM CT-SCAN IMAGES

Abstract

Coronavirus Disease 2019 (COVID-19) continues to pose a significant global health threat, necessitating accurate and efficient diagnostic methods. Manual analysis of CT scans is time-consuming and susceptible to errors, emphasizing the need for automated diagnostic tools. This paper presents a novel Custom Wide and Deep Neural Network (WDNN) developed from scratch for the binary classification of COVID-19 and non-COVID CT-scan images. Unlike conventional approaches that leverage transfer learning with pre-trained models such as VGG19, ResNet50, and InceptionV3, our architecture is fully data-driven and domain-specific. The model integrates a dual-branch structure combining a wide input layer and a deep convolutional path enhanced by a custom ExpandDimLayer. Real-time data augmentation techniques and grayscale preprocessing were employed to improve robustness and generalization. Evaluations were conducted on a comprehensive dataset of 15,000 2D CT-scan slices sourced from Kaggle public repository and Lakeview Hospital, Belagavi, organized into COVID and non-COVID categories. The proposed Custom-built Wide and Deep Neural Network model achieved exceptional performance with 99.49% accuracy, 99.31% precision, 99.68% recall, and 99.47% F1-score through rigorous 5-fold cross-validation, significantly outperforming existing transfer learning-based models. The model includes real-time prediction capabilities with Grad-CAM visualization for improved clinical interpretability. These results confirm the effectiveness of custom-built deep learning architectures for COVID-19 detection and broader medical imaging applications.