Detection of COVID-19 findings by the local interpretable model-agnostic explanations method of types-based activations extracted from CNNs.

[Display omitted] • Types detection between COVID-19 findings. • Application of Fourier Transform and Grad-CAM techniques in the preprocessing step. • Obtaining as many types-based activation sets as the number of classes for each CNN. • Applying the local interpretable model-agnostic explanations to the activation sets. • Reclassification of selected types-based efficient sets using the Softmax method. 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. [ABSTRACT FROM AUTHOR]