[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119282-en":3,"doc-seo-119282-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119282,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","A Comprehensive Review on Cancer Detection and Classification Using Medical Images by Machine Learning and Deep Learning","Machine learning and deep learning enable healthcare systems to support earlier and more accurate cancer detection and classification from medical images. The review provides a quick overview of cancer types and concentrates on ML/DL techniques for identifying multiple cancers, enabling image-based diagnosis, early intervention, and timely treatment. It discusses methodologies for predicting cancer using low-dose computed tomography and narrows further to lung cancer, addressing limitations of existing detection models and emphasizing the need for deeper study of novel algorithms. The review also highlights data setup for lung cancer and the potential value of genetic markers for stabilizing model accuracy.","INTERNATIONAL JOURNAL ON INFORMATICS VISUALIZATION  \n[journal homepage : www.joiv.org/index.php/joiv](journal homepage : www.joiv.org/index.php/joiv)  \nA Comprehensive Review on Cancer Detection and Classification Using Medical Images by Machine Learning and Deep Learning  \nModels  \nJayapradha J a,b, Su-Cheng Hawb,*, Palanichamy Naveenb, Elham Anaamb  \na Department of Computing Technologies, School of Computing, SRM Institute of Science and Technology, Kattankulathur, India b Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Selangor, Malaysia Corresponding author:*[sucheng@mmu.edu.my](sucheng@mmu.edu.my)  \nAbstract—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.  \nKeywords—Machine learning; deep learning; healthcare; cancer; medical images; lung cancer.  \nManuscript received 11 Jul. 2024; revised 2 Sep. 2024; accepted 23 Oct. 2024. Date of publication 30 Nov. 2024.  \nInternational Journal on Informatics Visualization is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.  \nI. INTRODUCTION  \nCancer has grown to be a serious public health concern and a silent killer of millions of people worldwide. It was determined that 14,61,427 cancer cases occurred in India in 2022. In India, around one in nine individuals will encounter cancer at some point in their lives[1] . With an expected ten million deaths in 2020, cancer was ranked as the second largest mortality rate in the World Health Organization's survey report [2] and more than 100 kinds of cancer according to its characteristics. The most common categories of cancer are (1) Bladder cancer, (2) Breast Cancer, (3) Colorectal Cancer, (4) Kidney Cancer, (5) Lung Cancer, (6) Lymphoma, (7) Pancreatic Cancer, (8)  \nProstate Cancer,(9) Skin cancer and (10) Uterine cancer[3]. Fig.  \n1 depicts the number of persons pretentious by diverse cancer categories as per the facts.  \nFig. 1 Number of persons pretentious by diverse cancer categories  \nThe number of American deaths from cancer is predicted tobe 609,820 in 2023. There are 127,070 predicted deaths from lung and bronchus cancer, making it the disease with the highest death toll in US. Fig. 2 displays the original cancer cases and cancer demises in 2023[4] .  \nFig. 2 Number of new cancer cases and cancer deaths in 2023  \nOur study is going to have a detailed erudition of five cancers such as (1) Breast cancer, (2) Brain tumor, (3) Cervical cancer (4) Skin cancer and (5) lung cancer. As our research is going to concentrate ","cbCaibTkb8DWuDbh","https://ap.wps.com/l/cbCaibTkb8DWuDbh","pdf",3703935,1,9,"English","en",105,"# Introduction\n## Cancer burden and global context\n## Cancer categories and common types\n## Focus on lung cancer\n## Role of imaging and AI in classification","[{\"question\":\"What is the main objective of the review?\",\"answer\":\"To provide a comprehensive overview of machine learning and deep learning techniques for cancer detection and classification using medical images, with additional focus on lung cancer.\"},{\"question\":\"Which cancer detection approaches are emphasized?\",\"answer\":\"The review emphasizes image-based classification using ML/DL, including methods that involve low-dose computed tomography, and outlines how AI supports early diagnosis and treatment planning.\"},{\"question\":\"Why does the review narrow down to lung cancer?\",\"answer\":\"It aims to address limitations in lung cancer detection models and stresses the need for deeper research into novel lung cancer detection algorithms.\"}]","A Comprehensive Review on Cancer Detection and Classification Using Medical Images by Machine Learning and Deep Learning | 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