An efficient approach to detect melanoma skin cancer using a custom CNN model
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.
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Brac University
2023
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| 在线阅读: | http://hdl.handle.net/10361/21833 |
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10361-218332023-10-16T21:04:06Z An efficient approach to detect melanoma skin cancer using a custom CNN model Rahman, K.M Saidur Amin, Tanjim Bin Rahman, Mahdi Sakib Sakib, G M Shadman Hossain Alam, Md. Ashraful Department of Computer Science and Engineering, Brac University Convolutional Neural Networks (CNN) Melanoma Skin cancer VGG-16 VGG-19 AlexNet Artificial intelligence--Medical applications Diagnostic imaging This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 35-38). Despite only making up 1% of all occurrences of skin cancer, melanoma is one of the most prevalent forms to cause fatalities in recent years. Melanoma has a survival rate of more than 50% from the early stages to the end. To survive this type of cancer, it is essential to identify lesions on the skin early and to keep an eye out for any complications. If skin cancer is not detected and treated early, it is among the most fatal cancers. Of the skin cancers, which are among the deadliest, melanoma is the most unexpected. Like most other diseases, melanoma may be treatable if caught early enough. Due to the high cost of having a dermatologist screen every patient and the difficulty of human judgment, an automated system for melanoma diagnosis is required. Due to its promising pattern recognition skills, Convolutional Neural Network (CNN) models have recently gained a lot of interest in medical imaging. Melanoma diagnosis from dermoscopic skin samples automatically is a difficult task. In contrast to other types, melanoma ranks as the most serious type of skin cancer. However, those who are diagnosed early on have a better prognosis; several methods of spontaneous melanoma recognition and diagnosis have been researched by different researchers for the objective of providing a supplementary opinion to professionals. Building models using existing data has proven problem- atic due to the imbalance between classes. However, these issues may be solved by implementing a deep learning approach as a machine vision tool. The purpose of the current study was to determine how well dermoscopy and deep learning classified melanoma. In this paper, we introduce a brand-new deep learning model that was created to categorize melanoma skin cancer. And we have compared the result of our suggested model with pre-trained VGG16, VGG19, and AlexNet. According to experimental data, we discovered that our model worked well and could accurately categorize melanoma skin cancer. Also, the proposed system is competitive in the area of melanoma detection and superior in terms of accuracy and can be employed in the clinical decision-making procedure for melanoma skin cancer early detection. K.M Saidur Rahman Tanjim Bin Amin Mahdi Sakib Rahman G M Shadman Hossain Sakib B.Sc. in Computer Science and Engineering 2023-10-16T04:34:04Z 2023-10-16T04:34:04Z ©2022 2022-09-28 Thesis ID 22241036 ID 17301220 ID 21101349 ID 17301099 http://hdl.handle.net/10361/21833 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. 51 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Convolutional Neural Networks (CNN) Melanoma Skin cancer VGG-16 VGG-19 AlexNet Artificial intelligence--Medical applications Diagnostic imaging |
| spellingShingle |
Convolutional Neural Networks (CNN) Melanoma Skin cancer VGG-16 VGG-19 AlexNet Artificial intelligence--Medical applications Diagnostic imaging Rahman, K.M Saidur Amin, Tanjim Bin Rahman, Mahdi Sakib Sakib, G M Shadman Hossain An efficient approach to detect melanoma skin cancer using a custom CNN model |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. |
| author2 |
Alam, Md. Ashraful |
| author_facet |
Alam, Md. Ashraful Rahman, K.M Saidur Amin, Tanjim Bin Rahman, Mahdi Sakib Sakib, G M Shadman Hossain |
| format |
Thesis |
| author |
Rahman, K.M Saidur Amin, Tanjim Bin Rahman, Mahdi Sakib Sakib, G M Shadman Hossain |
| author_sort |
Rahman, K.M Saidur |
| title |
An efficient approach to detect melanoma skin cancer using a custom CNN model |
| title_short |
An efficient approach to detect melanoma skin cancer using a custom CNN model |
| title_full |
An efficient approach to detect melanoma skin cancer using a custom CNN model |
| title_fullStr |
An efficient approach to detect melanoma skin cancer using a custom CNN model |
| title_full_unstemmed |
An efficient approach to detect melanoma skin cancer using a custom CNN model |
| title_sort |
efficient approach to detect melanoma skin cancer using a custom cnn model |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/21833 |
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