Deep learning based detection of silicosis from computed tomography images

Artificial intelligence has increasingly been used in interpreting medical images to support the timely treatment of diseases by providing early and accurate diagnosis. Pneumoconiosis is a tissue reaction that develops as a result of the accumulation of inorganic dust in the lungs. The most common types of pneumoconiosis include diseases such as coal worker's pneumoconiosis, silicosis, asbestosis, and siderosis. Silicosis, which has maintained its importance since the 1900s and has seen over 182,000 articles published in the last 10 years, is a global health problem. The automated detection and recognition of silicosis in lung computed tomography (CT) images can be considered the backbone of assisting the silicosis diagnosis process. Automated medical assistance systems developed using artificial intelligence can simplify the medical examination process and reduce the time required to start accurate treatment. Although the literature contains various studies that benefit silicosis diagnosis using chest X-ray images or pneumoconiosis diagnosis using CT images, there is not enough classification study that can particularly aid the diagnosis of silicosis in CT images. The method of early detection of silicosis from chest radiographs and CT images has been a challenging task due to the high variability among pneumoconiosis readers. Based on the success of deep learning in the classification and segmentation of medical images, this study has shown that deep learning networks and transfer learning algorithms can detect silicosis with high accuracy by classifying CT images. The performance of the six algorithms examined in the study is compared, and the algorithm with the best performance is recommended. Performance criteria such as accuracy, precision, specificity, and F1-score of the algorithms used in the study were calculated. The accuracy rates of the models were obtained as 92.62 %, 93.03 %, 92.76 %, 95.38 %, 97.29 %, and 95.17 % for AlexNet, VGG16, ResNet50, InceptionV3, Xception, and DenseNet121, respectively. These results show that Xception outperformed the other algorithms and was the most successful algorithm in the automatic detection of silicosis with an accuracy rate of 97.29 %. Additionally, a new dataset consisting of tomography images from silicosis patients is presented in this study. Experimental results have shown that transfer learning algorithms can significantly benefit the diagnosis of silicosis by successfully classifying CT images. The findings of the study highlight the clinical importance of artificial intelligence methods in medical image analysis and early disease diagnosis.