We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Deep Attention Network for Pneumonia Detection Using Chest X-Ray Images.
- Authors
Singh, Sukhendra; Rawat, Sur Singh; Gupta, Manoj; Tripathi, B. K.; Alanzi, Faisal; Majumdar, Arnab; Khuwuthyakorn, Pattaraporn; Thinnukool, Orawit
- Abstract
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 attentionaware CNN architecture for classifying chest X-ray images to diagnose Pneumonia in order to address the aforementioned difficulties. AttentionModules provide attention-aware properties to the Attention Network. The attentionaware 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 networkwas built by merging channel and spatial attentionmodules 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 95.47% and an F-score of 0.92, indicating that the suggested model outperformed against the baseline models.
- Subjects
X-rays; X-ray imaging; CONVOLUTIONAL neural networks; OBJECT recognition (Computer vision); COMPUTER vision; IMAGE recognition (Computer vision)
- Publication
Computers, Materials & Continua, 2023, Vol 75, Issue 1, p1673
- ISSN
1546-2218
- Publication type
Article
- DOI
10.32604/cmc.2023.032364