Framework to Detect COVID-19 from Chest X-Ray/CT using Deep-Learning and Arithmetic-Optimization Algorithm
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| Title: | Framework to Detect COVID-19 from Chest X-Ray/CT using Deep-Learning and Arithmetic-Optimization Algorithm |
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| Authors: | Fadiyah Almutairi |
| Source: | Advances in Artificial Intelligence and Machine Learning. :3476-3494 |
| Publisher Information: | Advances in Artificial Intelligence and Machine Learning, 2025. |
| Publication Year: | 2025 |
| Description: | 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 encompass; 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. |
| Document Type: | Article |
| ISSN: | 2582-9793 |
| DOI: | 10.54364/aaiml.2025.51199 |
| Accession Number: | edsair.doi...........6fd499c5f197dca07f3a3f5f8caf6aad |
| Database: | OpenAIRE |
Nájsť tento článok vo Web of Science | 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 encompass; 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. |
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| ISSN: | 25829793 |
| DOI: | 10.54364/aaiml.2025.51199 |