Citation

Jayaram, Jayapradha and Haw, Su Cheng and Naveen, Palanichamy and Anaam, Elham Abdulwahab (2024) A Comprehensive Review on Cancer Detection and Classification using Medical Images by Machine Learning and Deep Learning Models. JOIV : International Journal on Informatics Visualization, 8 (3-2). p. 1590. ISSN 2549-9610

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Abstract

In day-to-day life, machine learning and deep learning plays a vital role in healthcare applications to predict various diseases such as cancer, heart attack, mental problem, Parkinson, etc. Among these diseases, cancer is the life-threatening disease that leads a human being to death. The primary aim of this study is to provide a quick overview of various cancers and provides a comprehensive overview of machine learning and deep learning techniques in the detection and classification of several types of cancers. The significance of machine learning and deep learning in detecting various cancers using medical images were concentrated in this study. It also discusses various machine learning and deep learning algorithms that lead to accurate classification of medical images, early diagnosis, and immediate treatment for the patients and explores the methodologies which has been used to predict the cancer with the help of low dose computer tomography to reduce cancer related deaths. As the study narrows down the research into lung cancer, it combats the findings limitations in lung cancer detection models and highlights the need for a deep study of novel cancer detection algorithms. In addition, the review also finds the role of setting up data in lung cancer and the potential of genetic markers in stabilizing the accuracy of machine learning models. Overall, this study gives valuable suggestions to achieve more accuracy in cancer detection and classification using machine learning and deep learning techniques.

Item Type:	Article
Uncontrolled Keywords:	Machine learning; deep learning; healthcare; cancer; medical images; lung cancer
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	03 Jan 2025 03:23
Last Modified:	03 Jan 2025 03:23
URII:	http://shdl.mmu.edu.my/id/eprint/13275

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