Explainable Model of Fusion Network With Enhanced Optimization Approach for Tuberculosis Diagnosis

Abstract

Tuberculosis, caused by Mycobacterium tuberculosis, is a lung-focused infectious disease with a profound impact on the respiratory system. Although advancements in healthcare have improved detection of various diseases, TB remains a global health issue, especially in developing regions. Efficient TB diagnostics can be achieved through the deep-learning-based image classification techniques. In this work, a novel hybrid model is introduced in which two approaches are proposed. In the first approach, TrioFusionNet integrates three Convolutional models known as ResNet-50, InceptionV3, and EfficientB4 to detect deep features. After deep feature set creation, Principal Component Analysis is employed to decrease the feature dimensionality. The second approach utilizes the EAMSO (Ensemble of AMS Optimization model) for feature selection. It combines the results from AEO (Artificial Ecosystem-based Optimization), MBO (Monarch butterfly optimization), and Seagull-Algorithm to form three optimized feature subsets. These subsets are correlated by ensemble model, and the optimal features from EAMSO are input into an Artificial Neural Network for classification. The TrioFusionNet + EAMSO model combines features from three CNNs and selects the best ones using an ensemble optimization method. This approach improves accuracy and provides clear explanations of the results, making it more effective than existing methods. The various models are compared for analyzing the performance of the classification using two benchmark CXR image datasets. The proposed model yields a remarkable average accuracy value of 98.80%. Furthermore, explainable Artificial Intelligence experiments validate the effectiveness of the proposed diagnostic approach.

Keywords

• Res-Net-50
• InceptionV3
• EfficientB4
• Grad-CAM
• optimization
• tuberculosis