التفاصيل البيبلوغرافية
| العنوان: |
An explainable transfer learning framework for multi-classification of lung diseases in chest X-rays |
| المؤلفون: |
Aryan Nikul Patel, Ramalingam Murugan, Gautam Srivastava, Praveen Kumar Reddy Maddikunta, Gokul Yenduri, Thippa Reddy Gadekallu, Rajeswari Chengoden |
| المصدر: |
Alexandria Engineering Journal, Vol 98, Iss , Pp 328-343 (2024) |
| بيانات النشر: |
Elsevier, 2024. |
| سنة النشر: |
2024 |
| المجموعة: |
LCC:Engineering (General). Civil engineering (General) |
| مصطلحات موضوعية: |
Computer-aided diagnosis systems, Transfer learning, EfficientNet-B4 architecture, Explainable artificial intelligence, Multi-disease lung classification, Engineering (General). Civil engineering (General), TA1-2040 |
| الوصف: |
In the field of medical imaging, the increasing demand for advanced computer-aided diagnosis systems is crucial in radiography. Accurate identification of various diseases, such as COVID-19, pneumonia, tuberculosis, and pulmonary lung nodules, holds vital significance. Despite substantial progress in the medical field, a persistent research gap necessitates the development of models that excel in precision and provide transparency in decision-making processes. In order to address this issue, this work introduces an approach that utilizes transfer learning through the EfficientNet-B4 architecture, leveraging a pre-trained model to enhance the classification performance on a comprehensive dataset of lung X-rays. The integration of explainable artificial intelligence (XAI), specifically emphasizing Grad-CAM, contributes to model interpretability by providing insights into the neural network’s decision-making process, elucidating the salient features and activation regions influencing multi-disease classifications. The result is a robust multi-disease classification system achieving an impressive 96% accuracy, accompanied by visualizations highlighting critical regions in X-ray images. This investigation not only advances the progression of computer-aided diagnosis systems but also sets a pioneering benchmark for the development of dependable and transparent diagnostic models for lung disease identification. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
1110-0168 |
| Relation: |
http://www.sciencedirect.com/science/article/pii/S1110016824004551; https://doaj.org/toc/1110-0168 |
| DOI: |
10.1016/j.aej.2024.04.072 |
| URL الوصول: |
https://doaj.org/article/e23edc9bb26a46bbb6ab3242a4532b15 |
| رقم الانضمام: |
edsdoj.23edc9bb26a46bbb6ab3242a4532b15 |
| قاعدة البيانات: |
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