AttCDCNet: Attention-enhanced Chest Disease Classification using X-Ray Images

TL;DR

AttCDCNet is a novel deep learning model that improves chest disease diagnosis from X-ray images by incorporating attention mechanisms, focal loss, and depth-wise convolution, achieving superior accuracy over existing models.

Contribution

The paper introduces AttCDCNet, an enhanced DenseNet121-based model with attention blocks, focal loss, and depth-wise convolution for improved chest disease classification from X-ray images.

Findings

Achieved 94.94% accuracy on COVID-19 dataset.

Outperformed original DenseNet121 in precision and recall.

Demonstrated effectiveness of attention and focal loss in medical image diagnosis.

Abstract

Chest X-rays (X-ray images) have been proven to be effective for the diagnosis of chest diseases, including Pneumonia, Lung Opacity, and COVID-19. However, relying on traditional medical methods for diagnosis from X-ray images is prone to delays and inaccuracies because the medical personnel who evaluate the X-ray images may have preconceived biases. For this reason, researchers have proposed the use of deep learning-based techniques to facilitate the diagnosis process. The preeminent method is the use of sophisticated Convolutional Neural Networks (CNNs). In this paper, we propose a novel detection model named \textbf{AttCDCNet} for the task of X-ray image diagnosis, enhancing the popular DenseNet121 model by adding an attention block to help the model focus on the most relevant regions, using focal loss as a loss function to overcome the imbalance of the dataset problem, and utilizing depth-wise convolution to reduce the parameters to make the model lighter. Through extensive experimental evaluations, the proposed model demonstrates exceptional performance, showing better results than the original DenseNet121. The proposed model achieved an accuracy, precision and recall of 94.94%, 95.14% and 94.53%, respectively, on the COVID-19 Radiography Dataset.