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State-of-the-Art Challenges and Perspectives in Multi-Organ Cancer Diagnosis via Deep Learning-Based Methods.

Saqib Ali¹, Jianqiang Li¹, Yan Pei²

¹Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.

|November 13, 2021

Summary

Early cancer detection is crucial for survival. This review highlights deep learning, particularly Convolutional Neural Networks (CNNs), as a promising approach for multi-organ cancer diagnosis, segmentation, and classification.

Keywords:

automated computer-aid diagnosis systems cancer diagnosis deep learning machine learning medical imaging

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Area of Science:

Medical Imaging and Diagnostics
Artificial Intelligence in Healthcare
Oncology

Background:

Cancer remains a leading global cause of mortality, necessitating advancements in early detection.
Timely diagnosis significantly improves patient survival rates and treatment planning.
Multi-organ cancer detection presents complex challenges in medical diagnostics.

Purpose of the Study:

To survey and analyze state-of-the-art approaches for multi-organ cancer detection, segmentation, and classification.
To review current research trends and future challenges in breast, brain, lung, and skin cancer domains.
To provide an objective overview of imaging techniques, databases, and literature from 2016-2021.

Main Methods:

Systematic review and comparative analysis of existing literature on multi-organ cancer diagnosis.
Focus on deep learning techniques, specifically Convolutional Neural Networks (CNNs).
Examination of widely employed imaging techniques and modalities.

Main Results:

Deep learning, particularly CNN-based methods, achieves promising results in over 70% of studies for early multi-organ cancer diagnosis.
Identified ongoing trends, challenges, and potential solutions in the field.
Provided insights into widely used imaging techniques and gold-standard databases.

Conclusions:

CNN-based deep learning approaches show significant potential for robust computer-aided diagnosis systems.
This review offers valuable information for researchers developing new cancer diagnosis systems.
Bridging the gap between diagnosis and treatment planning is essential for improving cancer patient outcomes.