Cancer Diagnosis Using Deep Learning: A Bibliographic Review.

Area of Science:

• Oncology
• Medical Imaging
• Artificial Intelligence

Background:

• Traditional cancer diagnosis methods (ABCD, seven-point, Menzies, pattern analysis) are historically significant but lack efficiency.
• Established evaluation criteria (ROC curve, AUC, F1 score, accuracy, etc.) are crucial for assessing diagnostic performance.
• There is a growing need for advanced, intelligent methods in cancer diagnosis to overcome the limitations of conventional techniques.

Purpose of the Study:

• To provide a comprehensive overview of cancer diagnosis, from basic principles to advanced AI applications.
• To introduce deep learning techniques and their potential in medical image analysis for cancer detection.
• To equip researchers with foundational knowledge and practical tools (Python code) for implementing AI in cancer diagnosis.

Main Methods:

• Review of conventional cancer classification techniques and diagnostic evaluation criteria.
• Introduction to the framework of machine learning in medical imaging, including pre-processing, segmentation, and post-processing.
• Detailed description of various deep learning models (CNNs, GANs, RNNs, etc.) with accompanying Python code examples.

Main Results:

• Conventional methods are discussed alongside their performance evaluation metrics.
• The potential of deep neural networks for intelligent image analysis in cancer diagnosis is highlighted.
• Successful applications of deep learning models for breast, lung, brain, and skin cancer are compiled.

Conclusions:

• Deep learning offers a promising avenue for developing more efficient and accurate cancer diagnostic tools.
• The manuscript serves as a foundational resource for researchers venturing into AI-driven cancer diagnosis.
• Practical implementation guidance through Python code empowers researchers to explore and apply these advanced algorithms.