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2000
Volume 19, Issue 3
  • ISSN: 2666-2558
  • E-ISSN: 2666-2566

Abstract

Introduction

Cancer is known as a deadly disease, which includes several types of cancer. Cancer cannot be cured without proper treatment. Also, it is crucial to detect cancer at an early stage. The objective of this study is to examine, assess, classify, and explore recent advancements in the detection of different human body cancer types, such as breast, brain, lung, liver, and skin cancer.

Methods

This study explores several tools and methods in machine learning, either supervised or unsupervised, and deep learning involved in treatment procedures. It also highlights current issues and provides directions for future research projects. In this review study, different advanced machine learning, deep learning and artificial intelligence algorithms are used for the detection and classification of different types of cancers, including breast, skin, lung cancer and brain tumor.

Results and Discussion

This paper reviews advanced techniques, standard dataset comparison and analysis of identification of skin, breast, lung cancer and brain tumors. It also evaluates these techniques from the perspectives of F-measure, sensitivity, specificity, accuracy, and precision.

Conclusion

This review article focuses on detecting cancer using machine learning techniques. Successive improvements and detection of cancer over the past decades are reviewed, covering various types of cancer-like breast, brain, lung, liver, skin, and others. This paper focuses on the usage of machine learning in the diagnosis, treatment, and improvement of cancer.

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2026-02-23

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Machine Learning based Cancer Detection and Classification: A Critical Review of Approaches and Performance
Recent Advances in Computer Science and Communications 19, E26662558360533 (2026); https://doi.org/10.2174/0126662558360533250221044123
/content/journals/rascs/10.2174/0126662558360533250221044123
/content/journals/rascs/10.2174/0126662558360533250221044123
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  • Article Type:     Review Article
Keyword(s): artificial intelligence; benchmark dataset; Cancer; image processing; machine learning; supervised learning

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