AttCDCNet: Attention-Enhanced Chest Disease Classification Using X-Ray Images

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 work, we introduce an innovative detection model named 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, utilizing the focal loss as a loss function to overcome the imbalance of the dataset problem, and utilizing depth-wise convolution to make the model lighter by reducing the parameters. The proposed model demonstrates novelty by effectively integrating these enhancements to address critical challenges in medical image classification, including dataset imbalance and computational efficiency. Through extensive experimental evaluations, the proposed model demonstrates exceptional performance, surpassing the original DenseNet121 and other state-of-the-art models. The proposed model achieved an accuracy, precision and recall of 94.94%, 95.14% and 94.53%, respectively, on the COVID-19 Radiography Dataset. Underscoring its potential to improve diagnostic accuracy and efficiency in real-world medical applications.