Clinical Report: A Comprehensive Review of Colon Cancer Classification Utilizing CNNs

Overview

This review examines the application of convolutional neural networks (CNNs) in colon cancer classification, identifying a significant increase in research focused on CNNs for colon cancer over the past five years, with a systematic analysis of various architectures.

Background

Colon cancer remains a leading cause of cancer-related morbidity and mortality globally, making accurate diagnosis essential. The integration of artificial intelligence, particularly convolutional neural networks, has been explored in medical image processing. There is a need for systematic reviews comparing CNN performance in colon cancer classification tasks.

Data Highlights

No numerical data or trial data available in the source material.

Key Findings

• Convolutional neural networks have been applied in medical image processing in oncology.
• Only 0.7% of CNN studies focus specifically on colon cancer classification.
• Over the past five years, 2,567 papers have explored state-of-the-art CNN variants for colon cancer diagnosis.
• The systematic review utilized the PRISMA method and the PICOS framework for study selection.
• Methodological quality and potential biases were assessed using the PROBAST framework.

Clinical Implications

Ongoing research and systematic reviews are essential to establish practices for implementing CNNs in clinical settings.

Conclusion

The review highlights the need for further research to optimize the application of CNNs in colon cancer classification.

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