A comprehensive review of deep learning in lung cancer

TL;DR

This paper reviews the evolution of cancer classification, emphasizing the need for advanced, intelligent methods like deep learning to improve lung cancer diagnosis accuracy.

Contribution

It offers a comprehensive overview of deep learning applications in lung cancer diagnosis, highlighting historical context and current challenges.

Findings

Traditional methods are ineffective for accurate diagnosis.

Deep learning offers promising improvements in classification accuracy.

The review identifies gaps and future directions in the field.

Abstract

To provide the reader with a historical perspective on cancer classification approaches, we first discuss the fundamentals of the area of cancer diagnosis in this article, including the processes of cancer diagnosis and the standard classification methods employed by clinicians. Current methods for cancer diagnosis are deemed ineffective, calling for new and more intelligent approaches.