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Classifications of Multispectral Colorectal Cancer Tissues Using Convolution Neural Network
Hawraa Haj-Hassan1, Ahmad Chaddad2, Youssef Harkouss3
1Laboratory of Conception, Optimization and Modelling of Systems, University of Lorraine, Metz, Lorraine, France; Faculty of Engineering, Lebanese University, Beirut, Lebanon.
Journal of Pathology Informatics
|April 13, 2017
Summary
Convolution neural networks (CNNs) accurately classify colorectal cancer (CRC) tissue types, achieving 99.17% accuracy in distinguishing benign hyperplasia, intraepithelial neoplasia, and carcinoma from biopsy images.
Area of Science:
- Oncology
- Medical Imaging
- Computational Pathology
Background:
- Colorectal cancer (CRC) is a significant global health concern, ranking as the third most common cancer.
- Early diagnosis of CRC through colon biopsy image analysis is crucial for improving treatment outcomes.
- Accurate identification of precancerous and cancerous tissues is essential for effective CRC management.
Purpose of the Study:
- To investigate the efficacy of Convolutional Neural Networks (CNNs) for classifying colorectal cancer (CRC) tissue types.
- To differentiate between benign hyperplasia (BH), intraepithelial neoplasia (IN), and carcinoma (Ca) using automated image analysis.
- To enhance the diagnostic accuracy of CRC through advanced machine learning techniques.
Main Methods:
- Retrospective analysis of multispectral biopsy images from 30 CRC patients.
- Segmentation of pathological tissue regions using an active contour model.
- Classification of tissue samples (BH, IN, Ca) utilizing a CNN with convolution, max-pooling, and fully-connected layers.
- Training and testing the CNN model on distinct datasets to evaluate performance.
Main Results:
- Achieved a high classification accuracy of 99.17% on segmented image regions.
- Demonstrated superior performance compared to traditional feature extraction and classification methods.
- Validated the effectiveness of the CNN model in identifying different stages of CRC tissue.
Conclusions:
- CNNs are highly effective for classifying colorectal cancer (CRC) tissue types.
- Pre-segmentation of regions of interest significantly enhances the performance of CNN-based CRC classification.
- This approach shows promise for improving the accuracy and efficiency of CRC diagnosis.