Automated multi-class classification of lung diseases from CXR-images using pre-trained convolutional neural networks

ABSTRACT

According to the World Health Organization (WHO), Pneumonia, COVID-19, Tuberculosis, and Pneumothorax are the leading death causes in the world. Coughing, sneezing, fever, and shortness of breath are common symptoms. To detect them, several tests such as molecular tests (RT-PCR), antigen tests, Monteux tuberculin skin test (TST), and complete blood count (CBC) tests are needed. But these are time-consuming processes and have an error rate of 20% and a sensitivity of 80%. So, radiographic tests like computed tomography (CT) and an X-ray are used to identify lung diseases with the help of a physician. But the risk of these lung diseases' diagnoses overlapping features in chest radiographs is a worry with chest X-ray or CT-scan images. To accurately classify one of four diseases with healthy images demands the automation of such a process. There is no method for identifying and categorizing these lung diseases. As a result, we were encouraged to use eight pre-trained convolutional neural networks (CNN) to classify various lung diseases into COVID-19, pneumonia, pneumothorax, tuberculosis, and normal images from the chest X-ray image dataset. This classification process is divided into two phases. In the training phase, the CNNs are trained with the Adam optimizer with a maximum epoch of 30 and a mini-batch size of 32. In the classification phase, these trained networks are used to classify diseases. In both phases, the dataset is color preprocessed, resized, and undergoes data augmentation. For this, we used eight pre-trained CNNs Alexnet,Darknet-19, Darknet-53, Densenet-201, Googlenet, InceptionResnetV2, MobilenetV2, and Resnet-18. Finally, we concluded that the best one to classify these diseases. Among these networks, Densenet-201, achieved the highest accuracy of 97.2%, 94.28% of sensitivity, and 97.92% of specificity for K=5. For K=10, it achieved 97.49% of accuracy, 95.57% of sensitivity, and 97.96% of specificity and for K=15, achieved 97.01% of accuracy, 96.71% of sensitivity, and 97.17% of specificity. Hence, the proposed method outperformed the existing state-of-the-art methods. Finally, our proposed research could aid clinicians in making quick conclusions concerning lung problems so that treatment can proceed. © 2022 Elsevier Ltd