Identification of Silicosis using Deep Learning Model on Computed Tomography Images

Silicosis is an occupational lung disease caused due to inhaling of silica dust. In the Proposed work deep learning model is developed to Diagnose and detect Silicosis using Computed tomography images. The symptoms of silicosis are similar to that of tuberculosis and pneumonia. Hence, it is often misdiagnosed. Various imaging methods are used to detect the presence of silicosis. Among them one such is CT. It offers a high level of detail, producing a 360-degree computerized view of the human body. Generated nine different deep learning models to predict Silicosis. Computed tomography images were collected from different case reports. Totally collected 1320 images of healthy-normal lung Computed tomography and 129 images of silicosis Computed tomography. At the end of the augmentation, had 1529 images for silicosis Computed tomography. Model generated with VGG Produced 99.4 % accuracy in detecting disease.