Abstract

A novel coronavirus, known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which caused the COVID-19 pandemic, first emerged in Wuhan, China. Subsequently, a large number of individuals were infected worldwide, resulting in significant mortality. At present, various types of pneumonia continue to occur, many of which also lead to fatalities. To address this issue, several studies have proposed different pneumonia prediction techniques. However, these approaches often lack crucial diagnostic details. To overcome these limitations, a Fisher-LeNet (F-LeNet)-based pneumonia classification method is developed. Initially, input computed tomography (CT) images are preprocessed using a theta-mapped Laplacian filter (TMLF) and a code map function. Following preprocessing, the lung, trachea, and bronchi are segmented. Centrilobular nodules are then segmented using polygonal approximation (PA), Jaccard-indexed transform-based snake contour (JITSC), and the alpha hull (AH) method. The thickening value is estimated from the segmented lung regions. Using the ordered mini batch k-means (OMBKM) algorithm, the images are clustered into normal and pneumonia categories based on the extracted values. Features are then extracted from the pneumonia cluster. The Poisson distributed coati optimization algorithm (PDCOA) is employed to select the most relevant features, which are subsequently used as input to the F-LeNet model for pneumonia classification. The proposed approach is assessed experimentally and contrasted with conventional baseline techniques using standard performance metrics. According to the outcomes, the proposed approach achieves superior accuracy and computational efficiency. Therefore, this method shows strong potential for improving pneumonia diagnosis and reducing pneumonia-related fatalities.

Keywords

Pneumonia classification, Computed tomography (CT) imaging, Fisher-LeNet (F-LeNet), Feature selection, Theta-mapped Laplacian filter and Jaccard-indexed transform-based snake contour.

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