TY  - EJOU
AU  - Singh, Sukhendra 
AU  - Rawat, Sur Singh 
AU  - Gupta, Manoj 
AU  - Tripathi, B. K. 
AU  - Alanzi, Faisal 
AU  - Majumdar, Arnab 
AU  - Khuwuthyakorn, Pattaraporn 
AU  - Thinnukool, Orawit 

TI  - Deep Attention Network for Pneumonia Detection Using Chest X-Ray Images
T2  - Computers, Materials \& Continua

PY  - 2023
VL  - 74
IS  - 1
SN  - 1546-2226

AB  - In computer vision, object recognition and image categorization have proven to be difficult challenges. They have, nevertheless, generated responses to a wide range of difficult issues from a variety of fields. Convolution Neural Networks (CNNs) have recently been identified as the most widely proposed deep learning (DL) algorithms in the literature. CNNs have unquestionably delivered cutting-edge achievements, particularly in the areas of image classification, speech recognition, and video processing. However, it has been noticed that the CNN-training assignment demands a large amount of data, which is in low supply, especially in the medical industry, and as a result, the training process takes longer. In this paper, we describe an attention-aware CNN architecture for classifying chest X-ray images to diagnose Pneumonia in order to address the aforementioned difficulties. Attention Modules provide attention-aware properties to the Attention Network. The attention-aware features of various modules alter as the layers become deeper. Using a bottom-up top-down feedforward structure, the feedforward and feedback attention processes are integrated into a single feedforward process inside each attention module. In the present work, a deep neural network (DNN) is combined with an attention mechanism to test the prediction of Pneumonia disease using chest X-ray pictures. To produce attention-aware features, the suggested network was built by merging channel and spatial attention modules in DNN architecture. With this network, we worked on a publicly available Kaggle chest X-ray dataset. Extensive testing was carried out to validate the suggested model. In the experimental results, we attained an accuracy of <b>95.47%</b> and an F- score of <b>0.92,</b> indicating that the suggested model outperformed against the baseline models.
KW  - Attention network; image classification; object detection; residual networks; deep neural network

DO  - 10.32604/cmc.2023.032364