Framework to Detect COVID-19 from Chest X-Ray/CT using Deep-Learning and Arithmetic-Optimization Algorithm
Year: 2025
Type: article
Abstract: The lung infection caused by COVID-19 constitutes a medical emergency, necessitating prompt detection and treatment to mitigate its effects. Clinical diagnosis and assessment of severity are typically conducted utilizing medical imaging practices, like chest X-rays and lung CT scans. Owing to its advantages, Deep-Learning (DL) frameworks are extensively developed to identify occurrences utilizing clinically obtained X-ray/CT data. This study plans to propose a novel framework for COVID-19 detection in X-ray/CT images utilizing the Lightweight Deep-Learning Model (LDLM). This framework included the LDLM features alongside the Local Binary Pattern (LBP) features to enhance detection efficacy. Additionally, to reduce the over-fitting problem, this framework utilized the Arithmetic-Optimization Algorithm (AA) for features reduction and fusion to enhance detection accuracy. The stages of this framework encompassÍž data collecting and preliminary adjustment, feature extraction utilizing LDLM, and LBP, feature selection by AA and serial feature concatenation, and classification employing five-fold cross-validation. The suggested framework was evaluated and validated using the benchmark image database, yielding accuracy >99% for both Xray and CT based examinations. These results validate that the proposed approach yields substantial outcomes on the selected benchmark database.
Author: Fadiyah Almutairi
Institution: Majmaah University
Language: English
Topic: COVID-19 diagnosis using AI
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W4409035682