Ordinal classification framework for multiclass grading of pneumoconiosis

Pneumoconiosis is an occupational lung disease caused by the inhalation of mineral dust particles. This preliminary study is among the first to explore deep learning classification classification classification of the four ordinal categories on the scale of profusion (concentration) of small opacities (0, 1, 2, or 3). We introduce hierarchical cross entropy (HCE) loss by employing a sequence of binary classification layers post-feature extraction, which ensures a more granular feature differentiation, resonating with the intrinsic ordering of severity. Comparing performance on a ResNet framework against 1) cross-entropy loss, 2) Mean-Squared Error (MSE) loss and 3) multi-task conditional loss, results show that our HCE loss obtains the highest accuracy of 71.4% on the test set, demonstrating preliminary evidence that ResNet model with hierarchical cross entropy loss can successfully be used to grade this disease.