Deep Attention Network for Pneumonia Detection Using Chest X-Ray Images

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. Attention Modules 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 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 95.47% and an F-score of 0.92, indicating that the suggested model outperformed against the baseline models.

A Review of Recent Advances in Deep Learning Models for Chest Disease Detection Using Radiography

“…Image enhancement and data-augmentation techniques were applied, which improved the performance of the introduced model, achieving an ACC of 97.20% on Pediatric-CXR and 87.30% on ChestX-ray8. Singh et al [100] proposed an attention mechanism-based DCNN model for the classification of CXR images into two classes (normal or pneumonia). ResNet50 with attention achieved the best results with an ACC of 95.73% using images from Pediatric-CXR dataset.…”