Archive | 2021
Classification of Asbestosis in CT Imaging Data Using Convolutional LSTM
Abstract
\n Asbestosis is a disease with a high rate of mismatch between the readers. In this study, we use a deep learning framework to develop a model that can check the presence and lesion of asbestosis using medical CT data. The data was collected from 469 patients who had been tested for asbestosis. Out of the 469 patients, 284 were tested negative for asbestosis, while 185 were asbestosis positive. A CT image of a supine positioned lung setting was used. This study sought to solve the problem of image classification of CT data through Convolutional LSTM. Long-term Recurrent Convolution Networks (LRCN), a model that uses video format data as input, has been applied in this study. The model showed 83.3% Accuracy, 81.578% true positive and 86% true negative. In addition, a model that can verify validity by assisting a specialist with a Grad-CAM that can visualize the judgment was also developed. We hope that the model of this study will be able to work with specialists by acting as a judgment assistant.