Yong, Ming Ping and Hum, Yan Chai and Lai, Khin Wee and Lee, Ying Loong and Goh, Choon-Hian and Yap, Wun-She and Tee, Yee Kai (2024) Histopathological cancer detection using intra-domain transfer learning and ensemble learning. IEEE Access, 12. pp. 1434-1457. ISSN 2169-3536, DOI https://doi.org/10.1109/ACCESS.2023.3343465.
Full text not available from this repository.Abstract
Cancer is a leading global cause of death. Histopathology image analysis is widely recognized as the gold standard for cancer diagnosis, playing a crucial role in early detection and reducing mortality rates. However, this diagnostic task is performed manually by pathologists, leading to high human errors and variabilities due to the huge number of images to screen and tissue complexities. With the emergence of deep learning, specifically the Convolutional Neural Network (CNN), in computer-aided diagnosis, there is a growing interest from the medical community to automate the labor-intensive manual image screening. Despite its promising performance, deep learning models still encounter challenges when it comes to extracting comprehensive histopathological features for optimal results. To tackle this issue, our study introduces deep learning models based on intra-domain transfer learning and ensemble learning. We evaluated these models on public histopathology datasets, including Gastric Histopathology Sub-size Image Database (GasHisSDB), Chaoyang colorectal, and Clinical Proteomic Tumor Analysis Consortium Clear Cell Renal Cell Carcinoma (CPTAC-CCRCC). Our models achieved state-of-the-art accuracy: 99.78% on GasHisSDB, 85.69% on Chaoyang, and 99.17% on CPTAC-CCRCC. These results highlight our models' ability to extract rich features and perform well on low-resolution histopathology images. Thus, our models have the potential to assist pathologists, reduce their workload, and enhance patient survival rates.
| Item Type: | Article |
|---|---|
| Funders: | UTAR Strategy Fund of Universiti Tunku Abdul Rahman (UTAR) |
| Uncontrolled Keywords: | Histopathology; Cancer; Transfer learning; Feature extraction; Deep learning; Task analysis; Ensemble learning; Detection algorithms; Convolutional neural networks; Cancer detection; convolutional neural network; deep learning; ensemble model; histopathology; transfer learning |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering > Biomedical Engineering Department |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 21 Jun 2024 03:09 |
| Last Modified: | 21 Jun 2024 03:09 |
| URI: | http://eprints.um.edu.my/id/eprint/44212 |
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