Skin cancer classification for seven types of skin lesions
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.
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Brac University
2024
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| 在线阅读: | http://hdl.handle.net/10361/23649 |
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10361-236492024-07-03T21:04:02Z Skin cancer classification for seven types of skin lesions Rahman, Md. Tawsifur Azad, Md. Siam Sadman Muhtasim, Ali Chakrabarty, Amitabha Department of Computer Science and Engineering, Brac University Convolutional neural network Machine learning Cancer ResNet50v2 Inception V3 GAN Disease detection Diagnostic imaging Neural networks (Computer science) Cancer--Diagnosis 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 47-48). Machine learning (ML) for skin lesion identification employs algorithms, notably convolutional neural networks (CNNs), to categorize and detect skin lesions, aiming to enhance early detection and treatment of skin cancer. CNNs, trained on diverse lesion images, excel in learning features for classification, often rivaling dermatologists’ accuracy. Recent studies demonstrate CNNs’ effectiveness, achieving accuracy comparable to or surpassing dermatologists. Ongoing research focuses on addressing challenges like dataset diversity and robust evaluation metrics. Despite obstacles, ML’s potential to enhance early melanoma detection remains significant, promising to save lives through improved diagnosis and treatment. Notably, our research explored a hybrid approach, combining ResNet50v2 and InceptionV3 models trained on GAN-generated data. This innovative strategy achieved a notable 77% accuracy, showcasing promising results in advancing muticlass skin lesion identification accuracy. Md. Tawsifur Rahman Md. Siam Sadman Azad Ali Muhtasim B.Sc. in Computer Science 2024-07-03T04:59:43Z 2024-07-03T04:59:43Z ©2023 2023-05 Thesis ID 20141027 ID 20141002 ID 17301163 http://hdl.handle.net/10361/23649 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. 58 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Convolutional neural network Machine learning Cancer ResNet50v2 Inception V3 GAN Disease detection Diagnostic imaging Neural networks (Computer science) Cancer--Diagnosis |
| spellingShingle |
Convolutional neural network Machine learning Cancer ResNet50v2 Inception V3 GAN Disease detection Diagnostic imaging Neural networks (Computer science) Cancer--Diagnosis Rahman, Md. Tawsifur Azad, Md. Siam Sadman Muhtasim, Ali Skin cancer classification for seven types of skin lesions |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. |
| author2 |
Chakrabarty, Amitabha |
| author_facet |
Chakrabarty, Amitabha Rahman, Md. Tawsifur Azad, Md. Siam Sadman Muhtasim, Ali |
| format |
Thesis |
| author |
Rahman, Md. Tawsifur Azad, Md. Siam Sadman Muhtasim, Ali |
| author_sort |
Rahman, Md. Tawsifur |
| title |
Skin cancer classification for seven types of skin lesions |
| title_short |
Skin cancer classification for seven types of skin lesions |
| title_full |
Skin cancer classification for seven types of skin lesions |
| title_fullStr |
Skin cancer classification for seven types of skin lesions |
| title_full_unstemmed |
Skin cancer classification for seven types of skin lesions |
| title_sort |
skin cancer classification for seven types of skin lesions |
| publisher |
Brac University |
| publishDate |
2024 |
| url |
http://hdl.handle.net/10361/23649 |
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AT rahmanmdtawsifur skincancerclassificationforseventypesofskinlesions AT azadmdsiamsadman skincancerclassificationforseventypesofskinlesions AT muhtasimali skincancerclassificationforseventypesofskinlesions |
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