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jhbmi 2024, 11(1): 43-59 |
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Oviesi S, Tarokh M J, Momeni M K. Automatic Diagnosis of Pulmonary Emphysema Using Optimized Unet-based Deep Neural Network. jhbmi 2024; 11 (1) :43-59
URL: http://jhbmi.ir/article-1-841-en.html
URL: http://jhbmi.ir/article-1-841-en.html
Ph.D. Student, Faculty of Industrial Engineering, K. N. Toosi University of Technology, Department of Information Technology Engineering, Tehran, Iran
Abstract: (413 Views)
Introduction: Pulmonary emphysema is one of the lung diseases that usually remains unknown until old age and does not have a definitive treatment. A quick diagnosis of this disease helps a lot to the people involved in this disease and prevents the growth of emphysema masses. This research tries to in early diagnosis of this disease with the help of deep learning methods.
Method: This research tries to diagnose this disease faster with the help of Unet neural network optimized with GPC meta-heuristic algorithm. The data of this research were collected from Imam Ali and Bu Ali Sina hospitals, Zahedan city, Sistan and Baluchistan province. The data include 300 pieces with emphysema, including 65 cases of CLE, 97 cases of PSE, 138 cases of PLE, and 45 cases of normal data. These data were analyzed by Unet deep neural network and GPC optimization algorithm, and finally, with the help of accuracy criteria, recall, specificity, and F-measure were compared and investigated with other methods.
Results: In this research, the criteria used have much better results compared to other emphysema diagnosis methods with the help of the optimized Unet network, with accuracy of 18.97, prediction of 40.98, sensitivity of 48.23, and f score of 97.50, respectively, which shows a faster, more accurate, and more effective diagnosis of this disease with the help of the proposed method.
Conclusion: Using the right deep learning methods in combination with strong optimization algorithms can enable faster and more accurate treatment of diseases.
Method: This research tries to diagnose this disease faster with the help of Unet neural network optimized with GPC meta-heuristic algorithm. The data of this research were collected from Imam Ali and Bu Ali Sina hospitals, Zahedan city, Sistan and Baluchistan province. The data include 300 pieces with emphysema, including 65 cases of CLE, 97 cases of PSE, 138 cases of PLE, and 45 cases of normal data. These data were analyzed by Unet deep neural network and GPC optimization algorithm, and finally, with the help of accuracy criteria, recall, specificity, and F-measure were compared and investigated with other methods.
Results: In this research, the criteria used have much better results compared to other emphysema diagnosis methods with the help of the optimized Unet network, with accuracy of 18.97, prediction of 40.98, sensitivity of 48.23, and f score of 97.50, respectively, which shows a faster, more accurate, and more effective diagnosis of this disease with the help of the proposed method.
Conclusion: Using the right deep learning methods in combination with strong optimization algorithms can enable faster and more accurate treatment of diseases.
Keywords: Artificial intelligence, Neural networks, Pulmonary emphysema, Image processing, Deep Learning
Type of Study: Original Article |
Subject:
Artificial Intelligence in Healthcare
Received: 2024/01/17 | Accepted: 2024/05/29
Received: 2024/01/17 | Accepted: 2024/05/29
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