Abstract
Covid-19 is a disease that affects the upper and lower respiratory tract and has fatal consequences in individuals. Early diagnosis of COVID-19 disease is important. Datasets used in this study were collected from hospitals in Istanbul. The first dataset consists of COVID-19, viral pneumonia, and bacterial pneumonia types. The second dataset consists of the following findings of COVID-19: ground glass opacity, ground glass opacity, and nodule, crazy paving pattern, consolidation, consolidation, and ground glass. The approach suggested in this paper is based on artificial intelligence. The proposed approach consists of three steps. As a first step, preprocessing was applied and, in this step, the Fourier Transform and Gradient-weighted Class Activation Mapping methods were applied to the input images together. In the second step, type-based activation sets were created with three different ResNet models before the Softmax method. In the third step, the best type-based activations were selected among the CNN models using the local interpretable model-agnostic explanations method and re-classified with the Softmax method. An overall accuracy success of 99.15% was achieved with the proposed approach in the dataset containing three types of image sets. In the dataset consisting of COVID-19 findings, an overall accuracy success of 99.62% was achieved with the recommended approach.
【저자키워드】 COVID-19, deep learning, Image processing, Chest CT findings, Medical decision support system, 【초록키워드】 Pneumonia, hospital, Local, Viral pneumonia, CNN, Accuracy, dataset, ground glass, disease, consolidation, Bacterial, Lower respiratory tract, Ground glass opacity, Activation, class, mapping, İstanbul, Affect, approach, consequence, selected, collected, applied, suggested, individuals, diagnosis of COVID-19, ResNet model, 【제목키워드】 detection, Local, CNN, Activation,