International Journal Of Engineering Technology And Management Sciences

AUTHOR(S)

Rajdeep Dey, Sayantika Bose, Nandini Ghosh, Shamba Chakraborty, Dr. Avijit Kumar Chaudhuri, Sulekha Das

DOI: https://doi.org/10.46647/ijetms.2023.v07i02.030

ABSTRACT
Machine Learning (ML) is a specific type of artificial intelligence that allows systems to learn from data and detect patterns without much human intervention. As technology expands, machine learning provides an exciting opportunity in the health sector to improve the accuracy of diagnoses, personalise health care. Cancer has been characterised as a heterogeneous disease consisting of many different subtypes. In cancer research, it has become a necessity to diagnose and prognosis the type of cancer as early as it can facilitate the subsequent clinical management of patients. To accomplish the detection of cancer properly there are certain techniques in Machine learning which have been widely applied in cancer research for the development of predictive models, resulting in effective and accurate decision making. SVM, KNN, DT, LR, CNN, ANN, RF, MLP etc are such types of techniques in ML to model the progression and treatment of cancerous conditions. Apart from ML techniques there are certain image processing tools that are being introduced to detect cancer. Getting a clear cut classification from a biopsy image is an inconvenient task as the pathologist must know the detailed features of a normal and the affected cells. In this paper we are going to review the effectiveness of these various kinds of ML approaches to detect different types of cancer in some previous research papers which have already been done. The analyses and assessment techniques of the selected papers are discussed and an appraisal of the findings presented to conclude the article.

Page No: 254 - 270

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How to Cite This Article:
Rajdeep Dey, Sayantika Bose, Nandini Ghosh, Shamba Chakraborty, Dr. Avijit Kumar Chaudhuri, Sulekha Das . An Extensive Review on Cancer Detection using Machine Learning Algorithms . ijetms;7(2):254-270. DOI: 10.46647/ijetms.2023.v07i02.030