An Advanced Convolutional Neural Network for Detecting Chest X-ray Abnormalities

In the field of medical images diagnoses, doctors need a valuable second opinion when diagnosing thoracic diseases in chest X-rays. Existing methods of interpreting chest X-ray images classify them into a list of findings without specifying their locations on the images, resulting in uninterpretable results. Convolutional Neural Network (CNN) is a popular model for thoracic diseases diagnoses, which is a deep learning technique that has shown high accuracy in image classification and feature detection. In this work, an advanced CNN model is proposed to identify 14 findings in chest X-rays. For each test image, the intended CNN model should predict a bounding box and class for all findings. The classes range from 0 to 13, with each number corresponding to a specific disease in the dataset. The results have demonstrated that the proposed model outperforms the CapsNet model with an accuracy of 94% in X-ray images classification and labeling.