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Review
. 2022 Nov 28;22(23):9250.
doi: 10.3390/s22239250.

Colon Cancer Diagnosis Based on Machine Learning and Deep Learning: Modalities and Analysis Techniques

Mai Tharwat  1 , Nehal A Sakr  1 , Shaker El-Sappagh  2   3 , Hassan Soliman  1 , Kyung-Sup Kwak  4 , Mohammed Elmogy  1
Affiliations
Review

Colon Cancer Diagnosis Based on Machine Learning and Deep Learning: Modalities and Analysis Techniques

Mai Tharwat et al. Sensors (Basel). .

Abstract

The treatment and diagnosis of colon cancer are considered to be social and economic challenges due to the high mortality rates. Every year, around the world, almost half a million people contract cancer, including colon cancer. Determining the grade of colon cancer mainly depends on analyzing the gland's structure by tissue region, which has led to the existence of various tests for screening that can be utilized to investigate polyp images and colorectal cancer. This article presents a comprehensive survey on the diagnosis of colon cancer. This covers many aspects related to colon cancer, such as its symptoms and grades as well as the available imaging modalities (particularly, histopathology images used for analysis) in addition to common diagnosis systems. Furthermore, the most widely used datasets and performance evaluation metrics are discussed. We provide a comprehensive review of the current studies on colon cancer, classified into deep-learning (DL) and machine-learning (ML) techniques, and we identify their main strengths and limitations. These techniques provide extensive support for identifying the early stages of cancer that lead to early treatment of the disease and produce a lower mortality rate compared with the rate produced after symptoms develop. In addition, these methods can help to prevent colorectal cancer from progressing through the removal of pre-malignant polyps, which can be achieved using screening tests to make the disease easier to diagnose. Finally, the existing challenges and future research directions that open the way for future work in this field are presented.

Keywords: colon cancer diagnosis; deep-learning techniques; histopathology image analysis; imaging modalities; medical image analysis.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The different stages of colon cancer.
Figure 2
Figure 2
The structure of the survey.
Figure 3
Figure 3
Frequency-based analysis of technique types in percentages.
Figure 4
Figure 4
Frequency-based analysis of sub-technique types in percentages.
Figure 5
Figure 5
Inclusion and exclusion criteria.
Figure 6
Figure 6
Different images of CT colonoscopy: (a) Axial. (b) Sagittal images. (c) Image of virtual colonoscopy. (d) Image colonoscopy showing the polyp with true-positive findings.
Figure 7
Figure 7
HI analysis pipeline.
Figure 8
Figure 8
Original data and associated manual annotation from ETIS-Larib polyp DB. (a) the original image and (b) the annotation.
Figure 9
Figure 9
(a–c) The original images. (d–f) The corresponding ground truth.
Figure 10
Figure 10
The main stages of conventional ML methods for HI analysis.
Figure 11
Figure 11
The segmentation and classification process using deep learning.
See this image and copyright information in PMC

References

    1. Allison J.E. Colorectal cancer screening guidelines: The importance of evidence and transparency. Gastroenterology. 2010;138:1648–1652. doi: 10.1053/j.gastro.2010.03.001. - DOI - PubMed
    1. An F.P., Liu J.E. Medical Image Segmentation Algorithm Based on Optimized Convolutional Neural Network-Adaptive Dropout Depth Calculation. Complexity. 2020;2020:1645479. doi: 10.1155/2020/1645479. - DOI
    1. Araghi M., Soerjomataram I., Jenkins M., Brierley J., Morris E., Bray F., Arnold M. Global trends in colorectal cancer mortality: Projections to the year 2035. Int. J. Cancer. 2019;144:2992–3000. doi: 10.1002/ijc.32055. - DOI - PubMed
    1. Barish M.A., Soto J.A., Ferrucci J.T. Consensus on current clinical practice of virtual colonoscopy. Am. J. Roentgenol. 2005;184:786–792. doi: 10.2214/ajr.184.3.01840786. - DOI - PubMed
    1. Thun M.J., Calle E.E., Namboodiri M.M., Flanders W.D., Coates R.J., Byers T., Boffetta P., Garfinkel L., Heath C.W., Jr. Risk factors for fatal colon cancer in a large prospective study. JNCI J. Natl. Cancer Inst. 1992;84:1491–1500. doi: 10.1093/jnci/84.19.1491. - DOI - PubMed