T-Fusion Net: A Novel Deep Neural Network Augmented with Multiple Localizations based Spatial Attention Mechanisms for Covid-19 Detection

TL;DR

This paper introduces T-Fusion Net, a deep neural network with spatial attention mechanisms and ensemble techniques, achieving high accuracy in Covid-19 CT scan classification.

Contribution

The paper presents a novel deep neural network with multiple localizations based spatial attention and an ensemble approach with fuzzy max fusion for Covid-19 detection.

Findings

T-Fusion Net achieves 97.59% accuracy on Covid-19 CT dataset.

Ensemble of T-Fusion Nets improves accuracy to 98.4%.

The proposed methods outperform existing state-of-the-art techniques.

Abstract

In recent years, deep neural networks are yielding better performance in image classification tasks. However, the increasing complexity of datasets and the demand for improved performance necessitate the exploration of innovative techniques. The present work proposes a new deep neural network (called as, T-Fusion Net) that augments multiple localizations based spatial attention. This attention mechanism allows the network to focus on relevant image regions, improving its discriminative power. A homogeneous ensemble of the said network is further used to enhance image classification accuracy. For ensembling, the proposed approach considers multiple instances of individual T-Fusion Net. The model incorporates fuzzy max fusion to merge the outputs of individual nets. The fusion process is optimized through a carefully chosen parameter to strike a balance on the contributions of the individual models. Experimental evaluations on benchmark Covid-19 (SARS-CoV-2 CT scan) dataset demonstrate the effectiveness of the proposed T-Fusion Net as well as its ensemble. The proposed T-Fusion Net and the homogeneous ensemble model exhibit better performance, as compared to other state-of-the-art methods, achieving accuracy of 97.59% and 98.4%, respectively.