A comparative analysis of deep learning and hybrid models to diagnose multi-class skin cancer

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.

Bibliographic Details
Main Authors:	Nawrin, Ishrat Nur, Trina, Tonusree Talukder
Other Authors:	Rasel, Annajiat Alim
Format:	Thesis
Language:	English
Published:	Brac University 2023
Subjects:	Hybrid models Lethal Skin cancer Deep learning Cognitive learning theory Cancer > Diagnosis Machine learning
Online Access:	http://hdl.handle.net/10361/22000

id	10361-22000
record_format	dspace
spelling	10361-220002023-12-18T21:02:35Z A comparative analysis of deep learning and hybrid models to diagnose multi-class skin cancer Nawrin, Ishrat Nur Trina, Tonusree Talukder Rasel, Annajiat Alim Rahman, Rafeed Department of Computer Science and Engineering, Brac University Hybrid models Lethal Skin cancer Deep learning Cognitive learning theory Cancer--Diagnosis Machine learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 25-26). Skin cancer is one of the most lethal and increasingly prevalent cancers in the world. Skin cancer develops when the epidermal (top layer of skin) cells divide abnormally, causing it to spread to other regions of the human body. Skin cancer exists in seven different varieties. The presence of malignant epidermal cells determines the type of skin cancer. Dermoscopy, spectroscopy, and imaging tests are primarily utilized to identify the malignancy. These procedures are expensive and prolonged. It may result in unfavorable effects such as bleeding, bruising, and infection as well. The narrow variances in multi class cancer pictures escalate the complexity of classification. Dermatologists confront challenges in the categorization of cancer types from images. Deep learning has resulted in a dramatic leap in disease identification. Deep learning models are capable of categorizing skin cancer more precisely than dermatologists. Several studies focused on pre-trained and hybrid models for categorizing the classes of skin cancer. In contrast to binary classification, the multi-class classification of skin cancer yielded an insignificant result for both deep learning and dermatologists. The proposed study employs varieties of deep learning and hybrid models to examine the performance of each model in categorizing the classes of cancer. The proposed CNN-SVM-LSTM hybrid model obtained the highest result compared to other models, with 87.15% accuracy, 87.42% precision, 87% recall, and 87.428% F1 score. To illustrate the overall comparison of the models, each model has been depicted through a classification report and a confusion matrix. Ishrat Nur Nawrin Tonusree Talukder Trina B.Sc. in Computer Science and Engineering 2023-12-18T05:05:05Z 2023-12-18T05:05:05Z 2023 2023-05 Thesis ID 19301160 ID 19301158 http://hdl.handle.net/10361/22000 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 26 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Hybrid models Lethal Skin cancer Deep learning Cognitive learning theory Cancer--Diagnosis Machine learning
spellingShingle	Hybrid models Lethal Skin cancer Deep learning Cognitive learning theory Cancer--Diagnosis Machine learning Nawrin, Ishrat Nur Trina, Tonusree Talukder A comparative analysis of deep learning and hybrid models to diagnose multi-class skin cancer
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.
author2	Rasel, Annajiat Alim
author_facet	Rasel, Annajiat Alim Nawrin, Ishrat Nur Trina, Tonusree Talukder
format	Thesis
author	Nawrin, Ishrat Nur Trina, Tonusree Talukder
author_sort	Nawrin, Ishrat Nur
title	A comparative analysis of deep learning and hybrid models to diagnose multi-class skin cancer
title_short	A comparative analysis of deep learning and hybrid models to diagnose multi-class skin cancer
title_full	A comparative analysis of deep learning and hybrid models to diagnose multi-class skin cancer
title_fullStr	A comparative analysis of deep learning and hybrid models to diagnose multi-class skin cancer
title_full_unstemmed	A comparative analysis of deep learning and hybrid models to diagnose multi-class skin cancer
title_sort	comparative analysis of deep learning and hybrid models to diagnose multi-class skin cancer
publisher	Brac University
publishDate	2023
url	http://hdl.handle.net/10361/22000
work_keys_str_mv	AT nawrinishratnur acomparativeanalysisofdeeplearningandhybridmodelstodiagnosemulticlassskincancer AT trinatonusreetalukder acomparativeanalysisofdeeplearningandhybridmodelstodiagnosemulticlassskincancer AT nawrinishratnur comparativeanalysisofdeeplearningandhybridmodelstodiagnosemulticlassskincancer AT trinatonusreetalukder comparativeanalysisofdeeplearningandhybridmodelstodiagnosemulticlassskincancer
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A comparative analysis of deep learning and hybrid models to diagnose multi-class skin cancer

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