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Cancer cell identification based on Oncology image classification using parallel transfer learning algorithms.

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Title: Cancer cell identification based on Oncology image classification using parallel transfer learning algorithms.
Authors: Umarani, S., Babu, M. Rajesh, Selvi, C. P. Thamil, Madhavan, Sruthi S
Source: Frontiers in Health Informatics; 2024, Vol. 13 Issue 3, p10930-10948, 19p
Subject Terms: MACHINE learning, ARTIFICIAL intelligence, DEEP learning, CONVOLUTIONAL neural networks, IMAGE recognition (Computer vision)
Abstract: Cancer is a very serious disease in which some cells in one part of the body start growing in a way that is not natural and form lumps. Predicting the condition, diagnosing it quickly and properly, and accurately predicting the prognosis are all necessary to lower the chance of mortality in this disease. Researchers from many different backgrounds have looked at how ML and Deep Learning techniques might be used in the fields of biology and bioinformatics to better categorize cancer patients into highand low-risk groups. Algorithms from the fields of artificial intelligence (AI), machine learning (ML), and deep learning (DL) are already being put to good use in the healthcare system. AI is a simulation of human intellect that makes predictions using data, rules, and knowledge that has been put into it. In the realms of machine learning and artificial intelligence, deep learning (DL) has found widespread use in fields as diverse as healthcare and the development of new medicines. As a result of the widespread availability of powerful computers, DL has become the go-to method of data analysis. We investigate how AI aids in cancer diagnosis and prognosis, focusing on its remarkable accuracy, which is even greater than that of conventional statistical applications in oncology.AlexNet, GoogleNet, and, DenseNet, convolutional neural networks (CNNs are just some of the methods used in the advancement of forecasting models for predicting a cure for cancer. We show how these techniques contribute to the field's progress as well.The experiments are carried out on three different datasets,Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD), Digital Database for Screening Mammography's Curated Breast Imaging Subset(CBIS-DDSM), and Brain MRIs. [ABSTRACT FROM AUTHOR]
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Database: Biomedical Index
Description
Abstract:Cancer is a very serious disease in which some cells in one part of the body start growing in a way that is not natural and form lumps. Predicting the condition, diagnosing it quickly and properly, and accurately predicting the prognosis are all necessary to lower the chance of mortality in this disease. Researchers from many different backgrounds have looked at how ML and Deep Learning techniques might be used in the fields of biology and bioinformatics to better categorize cancer patients into highand low-risk groups. Algorithms from the fields of artificial intelligence (AI), machine learning (ML), and deep learning (DL) are already being put to good use in the healthcare system. AI is a simulation of human intellect that makes predictions using data, rules, and knowledge that has been put into it. In the realms of machine learning and artificial intelligence, deep learning (DL) has found widespread use in fields as diverse as healthcare and the development of new medicines. As a result of the widespread availability of powerful computers, DL has become the go-to method of data analysis. We investigate how AI aids in cancer diagnosis and prognosis, focusing on its remarkable accuracy, which is even greater than that of conventional statistical applications in oncology.AlexNet, GoogleNet, and, DenseNet, convolutional neural networks (CNNs are just some of the methods used in the advancement of forecasting models for predicting a cure for cancer. We show how these techniques contribute to the field's progress as well.The experiments are carried out on three different datasets,Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD), Digital Database for Screening Mammography's Curated Breast Imaging Subset(CBIS-DDSM), and Brain MRIs. [ABSTRACT FROM AUTHOR]
ISSN:26767104