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Classification of the Confocal Microscopy Images of Colorectal Tumor and Inflammatory Colitis Mucosa Tissue Using Deep Learningopen access

Authors
Jeong, JaehoonHong, Seung TaekUllah, IhsanKim, Eun SunPark, Sang Hyun
Issue Date
2월-2022
Publisher
MDPI
Keywords
colorectal neoplasm; colorectal inflammation; confocal microscopy; deep learning; machine learning
Citation
DIAGNOSTICS, v.12, no.2
Indexed
SCIE
SCOPUS
Journal Title
DIAGNOSTICS
Volume
12
Number
2
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/143905
DOI
10.3390/diagnostics12020288
ISSN
2075-4418
Abstract
Confocal microscopy image analysis is a useful method for neoplasm diagnosis. Many ambiguous cases are difficult to distinguish with the naked eye, thus leading to high inter-observer variability and significant time investments for learning this method. We aimed to develop a deep learning-based neoplasm classification model that classifies confocal microscopy images of 10x magnified colon tissues into three classes: neoplasm, inflammation, and normal tissue. ResNet50 with data augmentation and transfer learning approaches was used to efficiently train the model with limited training data. A class activation map was generated by using global average pooling to confirm which areas had a major effect on the classification. The proposed method achieved an accuracy of 81%, which was 14.05% more accurate than three machine learning-based methods and 22.6% better than the predictions made by four endoscopists. ResNet50 with data augmentation and transfer learning can be utilized to effectively identify neoplasm, inflammation, and normal tissue in confocal microscopy images. The proposed method outperformed three machine learning-based methods and identified the area that had a major influence on the results. Inter-observer variability and the time required for learning can be reduced if the proposed model is used with confocal microscopy image analysis for diagnosis.
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