Performance Comparison of Deep Learning Models for Black Lung Detection on Chest X-ray Radiographs

Abstract

Black Lung (BL) is an incurable respiratory disease caused by long term inhalation of respirable coal dust. Confidentiality restrictions and disease incidence limit the availability of BL datasets, which presents significant challenges in the training of deep learning (DL) models. This paper presents the implementations and detailed performance comparison of seven DL models for BL detection with small datasets. The models include VGG16, VGG19, InceptionV3, Xception, ResNet50, DenseNet121 and CheXNet. A small BL dataset of real and synthetic images was used to train the seven deep learning models. Segmented lung X-ray images, with and without BL, were used as training images to establish a benchmark. To increase the number of images required for training a deep learning system the training data set was augmented, using a Cycle-Consistent Adversarial Networks (CycleGAN) and the Keras Image Data Generator, to generate additional augmented and synthetic radiographs. The effects of different dropout nodes as a blocking factor was also investigated on all seven models. The best sensitivity (Normal Prediction Rate), specificity (BL prediction Rate), error rate (ERR or incorrect prediction rate), accuracy (1-ERR), as well as total execution time for binary classification for each model, with and without augmentation, was compared for optimal BL detection. On average, the CheXNet model gave the best performance of all seven DL models.