Empowering diagnosis: an astonishing deep transfer learning approach with fine tuning for precise lung disease classification from CXR images
A fast and precise diagnosis is crucial for the treatment and management of lung diseases, which are a major global cause of morbidity and mortality. Medical diagnosis and treatment planning depend heavily on the classification of lung diseases. The correct diagnosis and classification of many lung...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-01-01
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| Series: | Automatika |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/00051144.2023.2290737 |
| Summary: | A fast and precise diagnosis is crucial for the treatment and management of lung diseases, which are a major global cause of morbidity and mortality. Medical diagnosis and treatment planning depend heavily on the classification of lung diseases. The correct diagnosis and classification of many lung disease types is crucial for effective management and treatment. Radiologists with training evaluate medical images subjectively in order to classify lung diseases using traditional approaches. This paper proposed an effective technique for classifying lung diseases from CXR images. For the accurate classification of lung disorders, three distinct fine-tuned models are proposed. The effectiveness of the suggested fine-tuned models was evaluated using a newly developed CXR image dataset. According to the experimental findings, the proposed fine-tuned models outperformed the existing lung disease categorization models the accuracy is 98%. The suggested approach can effectively be used for lung disease classification. |
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| ISSN: | 0005-1144 1848-3380 |