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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...

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Autores principales: Yong, Ming Ping, Hum, Yan Chai, Lai, Khin Wee, Lee, Ying Loong, Goh, Choon-Hian, Yap, Wun-She, Tee, Yee Kai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10217020/
https://www.ncbi.nlm.nih.gov/pubmed/37238277
http://dx.doi.org/10.3390/diagnostics13101793
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author Yong, Ming Ping
Hum, Yan Chai
Lai, Khin Wee
Lee, Ying Loong
Goh, Choon-Hian
Yap, Wun-She
Tee, Yee Kai
author_facet Yong, Ming Ping
Hum, Yan Chai
Lai, Khin Wee
Lee, Ying Loong
Goh, Choon-Hian
Yap, Wun-She
Tee, Yee Kai
author_sort Yong, Ming Ping
collection PubMed
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.
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spelling pubmed-102170202023-05-27 Histopathological Gastric Cancer Detection on GasHisSDB Dataset Using Deep Ensemble Learning Yong, Ming Ping Hum, Yan Chai Lai, Khin Wee Lee, Ying Loong Goh, Choon-Hian Yap, Wun-She Tee, Yee Kai Diagnostics (Basel) Article 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. MDPI 2023-05-18 /pmc/articles/PMC10217020/ /pubmed/37238277 http://dx.doi.org/10.3390/diagnostics13101793 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yong, Ming Ping
Hum, Yan Chai
Lai, Khin Wee
Lee, Ying Loong
Goh, Choon-Hian
Yap, Wun-She
Tee, Yee Kai
Histopathological Gastric Cancer Detection on GasHisSDB Dataset Using Deep Ensemble Learning
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 Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10217020/
https://www.ncbi.nlm.nih.gov/pubmed/37238277
http://dx.doi.org/10.3390/diagnostics13101793
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