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Deep learning in cancer diagnosticsa feature-based transfer learning evaluation /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep learning in cancer diagnosticsby Mohd Hafiz Arzmi ... [et al.].
Reminder of title:	a feature-based transfer learning evaluation /
other author:	Arzmi, Mohd Hafiz.
Published:	Singapore :Springer Nature Singapore :2023.
Description:	x, 34 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	CancerDiagnosis
Online resource:	https://doi.org/10.1007/978-981-19-8937-7
ISBN:	9789811989377$q(electronic bk.)

Deep learning in cancer diagnosticsa feature-based transfer learning evaluation /
Deep learning in cancer diagnosticsa feature-based transfer learning evaluation /[electronic resource] :by Mohd Hafiz Arzmi ... [et al.]. - Singapore :Springer Nature Singapore :2023. - x, 34 p. :ill., digital ;24 cm. - SpringerBriefs in applied sciences and technology. Forensic and medical bioinformatics. - SpringerBriefs in applied sciences and technology.Forensic and medical bioinformatics..

1. Epidemiology, detection and management of cancer -- 2. A VGG16 feature-based Transfer Learning Evaluation for the diagnosis of Oral Squamous Cell Carcinoma (OSCC) -- 3. The Classification of Breast Cancer: The effect of hyperparameter optimisation towards the efficacy of feature-based transfer learning pipeline -- 4. The Classification of Lung Cancer: A DenseNet feature-based Transfer Learning Evaluation -- 5. Skin Cancer Diagnostics: A VGG Ensemble Approach -- 6. The Way Forward.

Cancer is the leading cause of mortality in most, if not all, countries around the globe. It is worth noting that the World Health Organisation (WHO) in 2019 estimated that cancer is the primary or secondary leading cause of death in 112 of 183 countries for individuals less than 70 years old, which is alarming. In addition, cancer affects socioeconomic development as well. The diagnostics of cancer are often carried out by medical experts through medical imaging; nevertheless, it is not without misdiagnosis owing to a myriad of reasons. With the advancement of technology and computing power, the use of state-of-the-art computational methods for the accurate diagnosis of cancer is no longer far-fetched. In this brief, the diagnosis of four types of common cancers, i.e., breast, lung, oral and skin, are evaluated with different state-of-the-art feature-based transfer learning models. It is expected that the findings in this book are insightful to various stakeholders in the diagnosis of cancer.

ISBN: 9789811989377$q(electronic bk.)

Standard No.: 10.1007/978-981-19-8937-7doiSubjects--Topical Terms:

887270
Cancer
--Diagnosis

LC Class. No.: RC270

Dewey Class. No.: 616.9940750285631

Deep learning in cancer diagnosticsa feature-based transfer learning evaluation /
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520 $a Cancer is the leading cause of mortality in most, if not all, countries around the globe. It is worth noting that the World Health Organisation (WHO) in 2019 estimated that cancer is the primary or secondary leading cause of death in 112 of 183 countries for individuals less than 70 years old, which is alarming. In addition, cancer affects socioeconomic development as well. The diagnostics of cancer are often carried out by medical experts through medical imaging; nevertheless, it is not without misdiagnosis owing to a myriad of reasons. With the advancement of technology and computing power, the use of state-of-the-art computational methods for the accurate diagnosis of cancer is no longer far-fetched. In this brief, the diagnosis of four types of common cancers, i.e., breast, lung, oral and skin, are evaluated with different state-of-the-art feature-based transfer learning models. It is expected that the findings in this book are insightful to various stakeholders in the diagnosis of cancer.
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