Call For Paper Volume: V, Issue: 08 | AUGUST 2026 | International Journal of Advanced Trends in Engineering and Management (IJATEM)
Volume II | Issue 7 | 2023 | Paper ID: IJATEM23JULY003

Explainable Pneumonia Classification using CoAtNet and Aquila Optimization for AI-enabled Healthcare Decision

Chest X-rays (CXRs) are usually used to diagnose pneumonia, that causes death all over the world, particularly in children under five. However, it is difficult to identify because skilled radiologists are required to gather up on microscopic patterns. Flexible replacement is provided by Artificial Intelligence (AI), which uses Deep Learning (DL) models to perform automated diagnosis. In this paper, AI-enabled Pneumonia classification using Convolutional and Attention Network (CoAtNet) integrated with Aquila Optimization (AO) is proposed for automated diagnosis of Pneumonia. Initially, CXR image is pre-processed using Bilateral Filter that aids in noise suppression in image by preventing image edges and structural information. Texture-based region growing segmentation process is deployed to precisely identify pneumonia-affected areas in CXR image by clustering nearby pixels with same textural characteristics. To extract texture characteristics related to pulmonary abnormalities, Local Binary Pattern (LBP) is employed. CoAtNet captures both local spatial pattern and global contextual patterns form CXR image, whereas AO performs optimization by selecting most informative features from extracted texture characteristics in CXR image. Following that, infections caused by pneumonia are differentiated from normal CXR image using optimal feature representation in which texture level and region level images abnormalities from CXR images are identified using CoAtNet and AO technique. Moreover, Gradient-weighted Class Activation Mapping (Grad-CAM) is merged to improve interpretability that highlights clinically significant regions that contribute classification decision. The proposed work is evaluated using python software and classification accuracy of 96.84%, precision of 96%, recall value of 96% and F1-score value of 95.98% are obtained.

Bilateral Filter, Local Binary Pattern (LBP), Aquila Optimization (AO), Grad-CAM and Explainable-Artificial Intelligence (XAI).

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