Mehdhar S. A. M. Al-gaashani

An efficient deep learning approach for colon cancer detection

• Authors Details :  
• Mehdhar S Al-gaashani

Journal title : Applied Sciences

Publisher : MDPI AG

Online ISSN : 2076-3417

Page Number : 8450

Journal volume : 12

Journal issue : 17

254 Views Original Article

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.

Article DOI & Crossmark Data

DOI : https://doi.org/10.3390/app12178450

Article Subject Details

• Artificial Intelligence
• Computer Science
• Computer Science Applications

Article Keywords Details

• CNN; colon cancer; deep learning; histopathological images; lightweight model

Article File

Full Text PDF

Article References

• (1). 10.1007/s12307-008-0007-2
• (2). 10.1080/01635589109514137
• (3). 10.1016/j.compbiomed.2021.104827
• (4). 10.1016/j.bspc.2022.103596
• (5). 10.1002/ima.22623
• (6). 10.3390/s21030748
• (7). 10.1016/j.compbiomed.2021.104730
• (8). 10.1038/srep27988
• (9). 10.1007/s11227-020-03575-6
• (10). 10.1016/j.imed.2021.08.003
• (11). 10.1109/BigDataCongress.2015.28
• (12). 10.1109/ECS.2015.7124883
• (13). 10.1016/j.compbiomed.2022.105409
• (14). 10.1016/j.ins.2021.05.035
• (15). 10.1109/TIM.2020.3033072
• (16). 10.1109/TMI.2020.2968770
• (17). 10.1016/j.jksus.2022.101898
• (18). 10.1016/j.bbe.2022.05.009
• (19). 10.1109/IWQoS.2018.8624183
• (20). 10.1109/ACCESS.2019.2962617
• (21). 10.1007/s00521-022-07524-7

---

More Article by Mehdhar S. A. M. Al-Gaashani