Title: LeNet-Xception: an advanced deep learning model for early COVID-19 detection from CT scan images

Authors: Kunte Noor Fathima; Renukalatha Shivrudraiah

Addresses: Sri Siddhartha Institute of Technology, Tumkur, A Constituent College of SSAHE, Maraluru, Kunigal Road, Tumakuru-572105, India ' Sri Siddhartha Institute of Technology, Tumkur, A Constituent College of SSAHE, Maraluru, Kunigal Road, Tumakuru-572105, India

Abstract: The COVID-19 pandemic has necessitated the deep learning, a subset of artificial intelligence, has had noteworthy development in the field of COVID-19 identification. Deep learning algorithms can analyse medical images, like CT scan images to aid in the swift and precise diagnosis of COVID-19. Deep learning models, such as LeNet and Xception, have been used in recent studies to diagnose COVID-19 from images of CT with high accuracy. This paper presents a deep learning approach for the detection of COVID-19 using computed tomography (CT) images by proposing a hybrid model, called LeNet-Xception. Various performance metrics, including specificity, sensitivity, and accuracy, were used to estimate the performance of the presented method. LeNet-Xception model attained an accuracy of 95.9%, a sensitivity of 97.5%, and a specificity of 93.8%. According to the results, the suggested technique suggests that can precisely identify cases of COVID-19 by utilising images of CT scans with high accuracy.

Keywords: COVID-19; CT images; disease detection; LeNet; deep learning.

DOI: 10.1504/IJBRA.2025.146349

International Journal of Bioinformatics Research and Applications, 2025 Vol.21 No.3, pp.234 - 255

Received: 10 Jan 2024
Accepted: 27 May 2024
Published online: 23 May 2025 *

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