A Bi-branch Dark Channel Differential Convolutional Neural Network for Occupational Pneumoconiosis Staging

Occupational pneumoconiosis (OP) staging is a vital task concerning the lung healthy of a subject. To perform artificial intelligence (AI)-assisted OP staging via chest X-ray image representational learning and classification commonly adopted to address it, where a Convolutional Neural Network (CNN) has proven to be efficient. However, unlike commonly encountered image classification tasks, OP staging relies heavily on the profusion level of opacities. The opacities in chest X-ray overlap with other tissues in the lung area and are hard to be represented by a standard CNN, thereby leading to inaccurate staging results. Aiming at implementing accurate determination of the opacities caused by pneumoconiosis, this study incorporates a dark channel prior method into a bi-branch learning structure, thereby establishing a Bi-branch Dark Channel Differential Convolutional Neural Network (BDCNN) for accurate AI-assisted OP staging. Its ideas are two-fold: a) extracting opacities caused by pneumoconiosis from chest X-ray with a dark channel prior-based dehazing method, and b) realizing multiple feature fusion via a bi-branch structure to ensure high staging accuracy. Experimental results on six real OP data cases demonstrate that the proposed BDCNN outperforms state-of-the-art models in obtaining accurate staging results for occupational pneumoconiosis.