Application of Linear Discriminant Analysis Method With Gray Level Cooccurrence Matrix Method for Classification of Lung Disease Diagnosis Based on X-Ray Results
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| Název: | Application of Linear Discriminant Analysis Method With Gray Level Cooccurrence Matrix Method for Classification of Lung Disease Diagnosis Based on X-Ray Results |
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| Autoři: | Nuriana Nuriana, Zahratul Fitri, Ar Razi |
| Zdroj: | Journal of Applied Informatics and Computing. 9:1406-1414 |
| Informace o vydavateli: | Politeknik Negeri Batam, 2025. |
| Rok vydání: | 2025 |
| Popis: | This study aims to classify lung diseases from X-ray images using a combination of Gray Level Cooccurrence Matrix (GLCM) and Linear Discriminant Analysis (LDA) methods. GLCM was used to extract texture features such as contrast, correlation, energy, and homogeneity from 300 lung X-ray images representing four categories: Normal, Pneumonia, Tuberculosis, and Bronchitis. The LDA method was then applied for classification based on these features. The results showed that Tuberculosis had the highest classification accuracy at 80%, while the overall model accuracy was 61.67%. Evaluation using precision, recall, F1-score, and confusion matrix confirmed that the GLCM and LDA combination performed best in identifying tuberculosis cases. However, overlapping features between Normal, Bronchitis, and Pneumonia classes reduced the classification performance. These findings suggest that the proposed method provides promising results and could be improved further with advanced feature extraction or classification techniques. |
| Druh dokumentu: | Article |
| ISSN: | 2548-6861 |
| DOI: | 10.30871/jaic.v9i4.9908 |
| Rights: | CC BY SA |
| Přístupové číslo: | edsair.doi...........958fcf15c3ad4e813e45241ad274da4d |
| Databáze: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstrakt: | This study aims to classify lung diseases from X-ray images using a combination of Gray Level Cooccurrence Matrix (GLCM) and Linear Discriminant Analysis (LDA) methods. GLCM was used to extract texture features such as contrast, correlation, energy, and homogeneity from 300 lung X-ray images representing four categories: Normal, Pneumonia, Tuberculosis, and Bronchitis. The LDA method was then applied for classification based on these features. The results showed that Tuberculosis had the highest classification accuracy at 80%, while the overall model accuracy was 61.67%. Evaluation using precision, recall, F1-score, and confusion matrix confirmed that the GLCM and LDA combination performed best in identifying tuberculosis cases. However, overlapping features between Normal, Bronchitis, and Pneumonia classes reduced the classification performance. These findings suggest that the proposed method provides promising results and could be improved further with advanced feature extraction or classification techniques. |
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| ISSN: | 25486861 |
| DOI: | 10.30871/jaic.v9i4.9908 |