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Gastric Cancer Classification from histopathological images using patch based Radiomic Features and Advanced Machine Learning Techniques
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논문 기본 정보

Type
Proceeding
Author
Shah Mahsoom ALi (Inje University) Tagne Poupi Theodore Armand (Inje University) Khadija Begum (Inje University) Hee-Cheol Kim (Inje University)
Journal
The Korea Institute of Information and Communication Engineering 한국정보통신학회 종합학술대회 논문집 Vol.27 No.2
Published
2023.10
Pages
540 - 544 (5page)

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Gastric Cancer Classification from histopathological images using patch based Radiomic Features and Advanced Machine Learning Techniques
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Gastric cancer, also known as stomach cancer, presents a significant health challenge, especially in East Asia, where it ranks among the leading causes of cancer-related death in countries like Japan, South Korea, and China. Early detection and precise characterization of gastric cancer are paramount for improving patient outcomes. In this study, we aim to harness the power of radiomic feature extraction through the Pyradiomics library to enhance the characterization of gastric cancer using medical imaging data collected from several Korean hospitals. Our research focuses on extracting key radiomic features, including Gray Level Co-occurrence Matrix (GLCM) features such as contrast, homogeneity, correlation, dissimilarity, and energy, as well as First Order Features like a energy, entropy, Mad, Root mean square and contrast. These extracted features served as the foundation for the classification process, wherein we employed advanced machine learning techniques. Leveraging radiomic features and advanced machine learning enhances interpretability and generalizability in gastric cancer characterization, complementing CNNs with comprehensive insights from smaller datasets and facilitating clinical data integration for improved predictive accuracy and computational efficiency in healthcare setting.
#Cancer Classification #Gastric Cancer #Radiomic Features #Advanced Machine Learning

Contents

ABSTRACT
Ⅰ Introduction:
Materials and Methods:
Gray Level Co-occurrence Matrix (GLCM) Features) :
Advanced Machine Learning:
Results and Discussions :
Model Evaluation:
Conclusion:
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