An Efficient Deep Learning Approach for Colon Cancer Detection
Source abstract
Abstract
Colon cancer is the second most common cause of cancer death in women and the third most common cause of cancer death in men. Therefore, early detection of this cancer can lead to lower infection and death rates. In this research, we propose a new lightweight deep learning approach based on a Convolutional Neural Network (CNN) for efficient colon cancer detection. In our method, the input histopathological images are normalized before feeding them into our CNN model, and then colon cancer detection is performed. The efficiency of the proposed system is analyzed with publicly available histopathological images database and compared with the state-of-the-art existing methods for colon cancer detection. The result analysis demonstrates that the proposed deep model for colon cancer detection provides a higher accuracy of 99.50%, which is considered the best accuracy compared with the majority of other deep learning approaches. Because of this high result, the proposed approach is computationally efficient.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: AI in cancer detection · COVID-19 diagnosis using AI · Colorectal Cancer Screening and Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Information System, Faculty of Computers and Information, Menoufia University, Shibin El Kom 32511, Egypt
Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Warszawska 24, 31-155 Krakow, Poland
Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Bałtycka 5, 44-100 Gliwice, Poland
Department of Electronics and Communications Engineering, Zagazig University, Zagazig 7120001, Egypt
Department of Information Technology, Faculty of Computers and Information, Menoufia University, Shibin El Kom 32511, Egypt
Funding
Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
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