Large-scale Histopathological Colon Cancer Annotation Model Using Machine Learning Techniques
Hamed, E., Tolba, M., Badr, N., Salem, M. (2023). Large-scale Histopathological Colon Cancer Annotation Model Using Machine Learning Techniques. EKB Journal Management System, 23(3), 73-82. doi: 10.21608/ijicis.2023.211720.1275
Esraa Abdelraouf Hamed; Mohamed Tolba; Nagwa Badr; Mohammed A.-M. Salem. "Large-scale Histopathological Colon Cancer Annotation Model Using Machine Learning Techniques". EKB Journal Management System, 23, 3, 2023, 73-82. doi: 10.21608/ijicis.2023.211720.1275
Hamed, E., Tolba, M., Badr, N., Salem, M. (2023). 'Large-scale Histopathological Colon Cancer Annotation Model Using Machine Learning Techniques', EKB Journal Management System, 23(3), pp. 73-82. doi: 10.21608/ijicis.2023.211720.1275
Hamed, E., Tolba, M., Badr, N., Salem, M. Large-scale Histopathological Colon Cancer Annotation Model Using Machine Learning Techniques. EKB Journal Management System, 2023; 23(3): 73-82. doi: 10.21608/ijicis.2023.211720.1275

Large-scale Histopathological Colon Cancer Annotation Model Using Machine Learning Techniques

International Journal of Intelligent Computing and Information Sciences
Volume 23, Issue 3, September 2023, Page 73-82  XML PDF (496.74 K)
Document Type: Original Article
DOI: 10.21608/ijicis.2023.211720.1275
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Authors
Esraa Abdelraouf Hamed email 1; Mohamed Tolbaorcid 2; Nagwa Badrorcid 3; Mohammed A.-M. Salem4
1Basic Science department, Faculty of computer and information sciences, Ain shams university, Cairo, Egypt
2Department of Scientific Computing, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, 11566, Egypt
3Department of Information Systems, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, 11566, Egypt
4Elnarges Buildings, 5th Settlement New Cairo
Abstract
Colon cancer ranks among the leading factors contributing to mortality and morbidity among adults. One of the main components in determining the kind of cancer is the histopathological diagnosis. This study presents the development of a computer-aided diagnosis system for adenocarcinomas of the colon using machine learning (ML) to analyze digital pathology images. A dataset of 10,000 images was gathered from the LC25000 collection, with 5000 images for each class. The Convolutional Neural Network with a Light Gradient Boosting Machine (CNN-LightGBM) with multiple threads was used as the classification model, and the system was evaluated against other ML algorithms. The reported diagnosis accuracy for colon cancer has achieved greater than 90%, outperforming the latest ML algorithms in disease classification accuracy. However, the accuracy was less than that for lung cancer classification based on this approach. This study demonstrates the potential for ML to improve the accuracy and efficiency of medical diagnosis and highlights the need for further research to improve the accuracy of colon cancer diagnosis.
Keywords
Colon cancer; Convolutional Neural Network; Deep Learning; Machine Learning; LightGBM
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