An efficient deep learning approach for colon cancer detection
Authors:
- Ahmed S. Sakr,
- Naglaa F. Soliman,
- Mehdhar S. Al-Gaashani,
- Paweł Pławiak,
- Abdelhamied A. Ateya,
- Mohamed Hammad
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.
- Record ID
- CUT0197fe117466494bb492cca69f6a0c87
- Publication categories
- ;
- Author
- Journal series
- Applied Sciences-Basel, ISSN 2076-3417, Monthly
- Issue year
- 2022
- Vol
- 12
- No
- 17
- Pages
- [1-13]
- Article number
- 8450
- Other elements of collation
- rys.; schem.; tab.; wykr.; Bibliografia (na s.) - 12-13; Bibliografia (liczba pozycji) - 24; Oznaczenie streszczenia - Abstr.; Numeracja w czasopiśmie - Vol. 12, Iss. 17, Spec. Iss.
- Substantive notes
- Special Issue: Advances in Medical Image Analysis and Computer-Aided Diagnosis
- Keywords in English
- CNN, colon cancer, deep learning, histopathological images, lightweight model
- ASJC Classification
- ; ; ; ; ;
- DOI
- DOI:10.3390/app12178450 Opening in a new tab
- URL
- https://www.mdpi.com/2076-3417/12/17/8450 Opening in a new tab
- Language
- eng (en) English
- License
- Score (nominal)
- 100
- Score source
- journalList
- Score
- Publication indicators
- Citation count
- 26
- Additional fields
- Indeksowana w: Web of Science, Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUT0197fe117466494bb492cca69f6a0c87/
- URN
urn:pkr-prod:CUT0197fe117466494bb492cca69f6a0c87
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.