An Efficient Deep Learning Approach for Colon Cancer Detection

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 Convol...

Full description

Saved in:
Bibliographic Details
Published in:Applied sciences 2022-09, Vol.12 (17), p.8450
Main Authors: Sakr, Ahmed S., Soliman, Naglaa F., Al-Gaashani, Mehdhar S., Pławiak, Paweł, Ateya, Abdelhamied A., Hammad, Mohamed
Format: Article
Language:English
Subjects:
Accuracy
Artificial intelligence
Automation
Big Data
Classification
CNN
Colon
Colon cancer
Colorectal cancer
Datasets
Death
Deep learning
Feature selection
histopathological images
Large intestine
lightweight model
Lymphatic system
Machine learning
Medical research
Methods
Mortality
Neural networks
Rectum
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app12178450