Farzane Tajidini - Tabarestan University of Chalus, Chalus, Iran

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. A cancer diagnosis is receiving more attention to define better diagnostic tools, thanks to artificial intelligence. Deep neural networks may be utilized for intelligent picture analysis with effectiveness. This paper presents the fundamental building blocks of machine learning's application to medical imaging—pre-processing, picture segmentation, and post-processing—. In the second section of this paper, all kinds of diseases have been investigated. We give deep learning for each methodology to enable interested readers to test out the described methods on their own diagnostic issues. This manuscript's final section gathers the effectively used deep learning models for various disease kinds. We limit our discussion to skin, lung, brain, and breast cancer due to the length of the paper.

_Deep Learning, Lung Cancer, Diagnosis, Intelligent Approaches