Benign and malignant detection and classification for small size image of breast tumor recognition system using u-net model
Breast tumor recognition is a critical task in the field of medical imaging systems, aiming to differentiate between benign and malignant tumors. To differentiate the tumors, an efficient technique is crucial to detect and classify it to avoid misdetection and misclassification, at the same time can...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
IEEE
2024
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| Online Access: | http://umpir.ump.edu.my/id/eprint/43028/ http://umpir.ump.edu.my/id/eprint/43028/2/Benign%20and%20Malignant%20Detection%20and%20Classification%20for%20Small%20Size%20Image%20of%20Breast%20Tumor%20Recognition%20System%20using%20U-Net%20Model%20-%20intro.pdf http://umpir.ump.edu.my/id/eprint/43028/1/Benign%20and%20Malignant%20Detection%20and%20Classification%20for%20Small%20Size%20Image%20of%20Breast%20Tumor%20Recognition%20System%20using%20U-Net%20Model.pdf |
| _version_ | 1848826762487660544 |
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| author | Suryanti, Awang Kumar, Saumya Nur Syafiqah, Mohd Nafis Raihanah, Haroon |
| author_facet | Suryanti, Awang Kumar, Saumya Nur Syafiqah, Mohd Nafis Raihanah, Haroon |
| author_sort | Suryanti, Awang |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Breast tumor recognition is a critical task in the field of medical imaging systems, aiming to differentiate between benign and malignant tumors. To differentiate the tumors, an efficient technique is crucial to detect and classify it to avoid misdetection and misclassification, at the same time can accelerate the process. Thus, this paper proposed a deep learning technique which is a modified architecture of U-net model that based on Convolutional Neural Network (CNN) to detect and classify the tumors. The aim is to have a less complex U-Net model that is effective for a small size of images. During the technique deployment, data augmentation, transfer learning, and ensemble approach are employed. The proposed technique is tested using Breast Ultrasound Images dataset (BUSI) that is available in Kaggle. The results obtained are promising with accuracy of 0.8, precision of 0.88, recall of 0.7, and F1-score of 0.8. It indicates that this technique can contribute to the advancement of breast tumor detection and classification by providing valuable insights for clinicians in making accurate and timely diagnoses. Thus, the proposed technique has the potential to improve the efficiency and effectiveness of breast tumor recognition, aiding in the early detection and treatment of breast cancer. |
| first_indexed | 2025-11-15T03:49:58Z |
| format | Conference or Workshop Item |
| id | ump-43028 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:49:58Z |
| publishDate | 2024 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-430282024-12-04T06:57:36Z http://umpir.ump.edu.my/id/eprint/43028/ Benign and malignant detection and classification for small size image of breast tumor recognition system using u-net model Suryanti, Awang Kumar, Saumya Nur Syafiqah, Mohd Nafis Raihanah, Haroon QA75 Electronic computers. Computer science Breast tumor recognition is a critical task in the field of medical imaging systems, aiming to differentiate between benign and malignant tumors. To differentiate the tumors, an efficient technique is crucial to detect and classify it to avoid misdetection and misclassification, at the same time can accelerate the process. Thus, this paper proposed a deep learning technique which is a modified architecture of U-net model that based on Convolutional Neural Network (CNN) to detect and classify the tumors. The aim is to have a less complex U-Net model that is effective for a small size of images. During the technique deployment, data augmentation, transfer learning, and ensemble approach are employed. The proposed technique is tested using Breast Ultrasound Images dataset (BUSI) that is available in Kaggle. The results obtained are promising with accuracy of 0.8, precision of 0.88, recall of 0.7, and F1-score of 0.8. It indicates that this technique can contribute to the advancement of breast tumor detection and classification by providing valuable insights for clinicians in making accurate and timely diagnoses. Thus, the proposed technique has the potential to improve the efficiency and effectiveness of breast tumor recognition, aiding in the early detection and treatment of breast cancer. IEEE 2024 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/43028/2/Benign%20and%20Malignant%20Detection%20and%20Classification%20for%20Small%20Size%20Image%20of%20Breast%20Tumor%20Recognition%20System%20using%20U-Net%20Model%20-%20intro.pdf pdf en http://umpir.ump.edu.my/id/eprint/43028/1/Benign%20and%20Malignant%20Detection%20and%20Classification%20for%20Small%20Size%20Image%20of%20Breast%20Tumor%20Recognition%20System%20using%20U-Net%20Model.pdf Suryanti, Awang and Kumar, Saumya and Nur Syafiqah, Mohd Nafis and Raihanah, Haroon (2024) Benign and malignant detection and classification for small size image of breast tumor recognition system using u-net model. In: 2024 5th International Conference on Artificial Intelligence and Data Sciences (AiDAS). 5th International Conference on Artificial Intelligence and Data Sciences (AiDAS) , 03-04 September 2024 , Bangkok, Thailand. pp. 386-391. (203744). ISBN 979-833152855-3 (Published) https://doi.org/10.1109/AiDAS63860.2024.10730254 |
| spellingShingle | QA75 Electronic computers. Computer science Suryanti, Awang Kumar, Saumya Nur Syafiqah, Mohd Nafis Raihanah, Haroon Benign and malignant detection and classification for small size image of breast tumor recognition system using u-net model |
| title | Benign and malignant detection and classification for small size image of breast tumor recognition system using u-net model |
| title_full | Benign and malignant detection and classification for small size image of breast tumor recognition system using u-net model |
| title_fullStr | Benign and malignant detection and classification for small size image of breast tumor recognition system using u-net model |
| title_full_unstemmed | Benign and malignant detection and classification for small size image of breast tumor recognition system using u-net model |
| title_short | Benign and malignant detection and classification for small size image of breast tumor recognition system using u-net model |
| title_sort | benign and malignant detection and classification for small size image of breast tumor recognition system using u-net model |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/43028/ http://umpir.ump.edu.my/id/eprint/43028/ http://umpir.ump.edu.my/id/eprint/43028/2/Benign%20and%20Malignant%20Detection%20and%20Classification%20for%20Small%20Size%20Image%20of%20Breast%20Tumor%20Recognition%20System%20using%20U-Net%20Model%20-%20intro.pdf http://umpir.ump.edu.my/id/eprint/43028/1/Benign%20and%20Malignant%20Detection%20and%20Classification%20for%20Small%20Size%20Image%20of%20Breast%20Tumor%20Recognition%20System%20using%20U-Net%20Model.pdf |