Histopathological Gastric Cancer Detection on GasHisSDB Dataset Using Deep Ensemble Learning
Gastric cancer is a leading cause of cancer-related deaths worldwide, underscoring the need for early detection to improve patient survival rates. The current clinical gold standard for detection is histopathological image analysis, but this process is manual, laborious, and time-consuming. As a res...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-05-01
|
| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/13/10/1793 |
| _version_ | 1797600386341666816 |
|---|---|
| author | Ming Ping Yong Yan Chai Hum Khin Wee Lai Ying Loong Lee Choon-Hian Goh Wun-She Yap Yee Kai Tee |
| author_facet | Ming Ping Yong Yan Chai Hum Khin Wee Lai Ying Loong Lee Choon-Hian Goh Wun-She Yap Yee Kai Tee |
| author_sort | Ming Ping Yong |
| collection | DOAJ |
| description | Gastric cancer is a leading cause of cancer-related deaths worldwide, underscoring the need for early detection to improve patient survival rates. The current clinical gold standard for detection is histopathological image analysis, but this process is manual, laborious, and time-consuming. As a result, there has been growing interest in developing computer-aided diagnosis to assist pathologists. Deep learning has shown promise in this regard, but each model can only extract a limited number of image features for classification. To overcome this limitation and improve classification performance, this study proposes ensemble models that combine the decisions of several deep learning models. To evaluate the effectiveness of the proposed models, we tested their performance on the publicly available gastric cancer dataset, Gastric Histopathology Sub-size Image Database. Our experimental results showed that the top 5 ensemble model achieved state-of-the-art detection accuracy in all sub-databases, with the highest detection accuracy of 99.20% in the 160 × 160 pixels sub-database. These results demonstrated that ensemble models could extract important features from smaller patch sizes and achieve promising performance. Overall, our proposed work could assist pathologists in detecting gastric cancer through histopathological image analysis and contribute to early gastric cancer detection to improve patient survival rates. |
| first_indexed | 2024-03-11T03:47:23Z |
| format | Article |
| id | doaj.art-20613af0046e404b8c4c50ae2ba7cca8 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-11T03:47:23Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-20613af0046e404b8c4c50ae2ba7cca82023-11-18T01:05:20ZengMDPI AGDiagnostics2075-44182023-05-011310179310.3390/diagnostics13101793Histopathological Gastric Cancer Detection on GasHisSDB Dataset Using Deep Ensemble LearningMing Ping Yong0Yan Chai Hum1Khin Wee Lai2Ying Loong Lee3Choon-Hian Goh4Wun-She Yap5Yee Kai Tee6Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang 43000, MalaysiaLee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang 43000, MalaysiaDepartment of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, MalaysiaLee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang 43000, MalaysiaLee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang 43000, MalaysiaLee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang 43000, MalaysiaLee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang 43000, MalaysiaGastric cancer is a leading cause of cancer-related deaths worldwide, underscoring the need for early detection to improve patient survival rates. The current clinical gold standard for detection is histopathological image analysis, but this process is manual, laborious, and time-consuming. As a result, there has been growing interest in developing computer-aided diagnosis to assist pathologists. Deep learning has shown promise in this regard, but each model can only extract a limited number of image features for classification. To overcome this limitation and improve classification performance, this study proposes ensemble models that combine the decisions of several deep learning models. To evaluate the effectiveness of the proposed models, we tested their performance on the publicly available gastric cancer dataset, Gastric Histopathology Sub-size Image Database. Our experimental results showed that the top 5 ensemble model achieved state-of-the-art detection accuracy in all sub-databases, with the highest detection accuracy of 99.20% in the 160 × 160 pixels sub-database. These results demonstrated that ensemble models could extract important features from smaller patch sizes and achieve promising performance. Overall, our proposed work could assist pathologists in detecting gastric cancer through histopathological image analysis and contribute to early gastric cancer detection to improve patient survival rates.https://www.mdpi.com/2075-4418/13/10/1793histopathologygastric cancerdeep learningconvolutional neural networktransfer learningensemble model |
| spellingShingle | Ming Ping Yong Yan Chai Hum Khin Wee Lai Ying Loong Lee Choon-Hian Goh Wun-She Yap Yee Kai Tee Histopathological Gastric Cancer Detection on GasHisSDB Dataset Using Deep Ensemble Learning Diagnostics histopathology gastric cancer deep learning convolutional neural network transfer learning ensemble model |
| title | Histopathological Gastric Cancer Detection on GasHisSDB Dataset Using Deep Ensemble Learning |
| title_full | Histopathological Gastric Cancer Detection on GasHisSDB Dataset Using Deep Ensemble Learning |
| title_fullStr | Histopathological Gastric Cancer Detection on GasHisSDB Dataset Using Deep Ensemble Learning |
| title_full_unstemmed | Histopathological Gastric Cancer Detection on GasHisSDB Dataset Using Deep Ensemble Learning |
| title_short | Histopathological Gastric Cancer Detection on GasHisSDB Dataset Using Deep Ensemble Learning |
| title_sort | histopathological gastric cancer detection on gashissdb dataset using deep ensemble learning |
| topic | histopathology gastric cancer deep learning convolutional neural network transfer learning ensemble model |
| url | https://www.mdpi.com/2075-4418/13/10/1793 |
| work_keys_str_mv | AT mingpingyong histopathologicalgastriccancerdetectionongashissdbdatasetusingdeepensemblelearning AT yanchaihum histopathologicalgastriccancerdetectionongashissdbdatasetusingdeepensemblelearning AT khinweelai histopathologicalgastriccancerdetectionongashissdbdatasetusingdeepensemblelearning AT yingloonglee histopathologicalgastriccancerdetectionongashissdbdatasetusingdeepensemblelearning AT choonhiangoh histopathologicalgastriccancerdetectionongashissdbdatasetusingdeepensemblelearning AT wunsheyap histopathologicalgastriccancerdetectionongashissdbdatasetusingdeepensemblelearning AT yeekaitee histopathologicalgastriccancerdetectionongashissdbdatasetusingdeepensemblelearning |