Explainable AI (XAI) driven skin cancer detection using transformer and CNN based architecture
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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| גישה מקוונת: | http://hdl.handle.net/10361/22837 |
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10361-228372024-05-15T21:00:26Z Explainable AI (XAI) driven skin cancer detection using transformer and CNN based architecture Radiah, Faiza Rahman, Kabasum Asadullah, Lasania Sohan, Md. Sohanur Rahman Ahmed, Jaki Alam, Md. Ashraful Reza, Md. Tanzim Department of Computer Science and Engineering, Brac University Skin cancer detection Disease detection CNN Deep learning Dermatoscopy VGG16 XAI Artificial intelligence--Medical applications Neural networks (Computer science) Deep learning (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 26-28). Skin Cancer is a cancer form that has become very prevalent in recent times and, if left untreated, has the potential to cause premature death. That is why early diagnosis and treatment are important to cure this disease. For this, we can use Machine Learning based methods to effectively impact the identification and categorization of skin cancer. Previously it was seen that the CNN models had a notable impact on the performance of the classification tasks. However, Vision transformers (VIT) are also the solution chosen by the researchers which have displayed significant performance in classification works. To make the outcomes of diverse data as distinct as feasible, contrastive learning is utilized to make similar skin cancer data for encoding similarly. The categorization of skin cancer depending upon multimodal data is made possible by the transformer network’s exceptional performance in natural language processing and field of vision. In this paper, we have offered a detailed analysis of VGG-16, a CNN architecture, and ViT, a transformer-based method to classify skin lesion images for aiding the early diagnosis of skin cancer. The findings indicate that the VGG-16 model attained an accuracy of 82.14%, whereas the Vision Transformer achieved a slightly lower accuracy of 76.15%. A modified version of the original vision transformer, the shifted patch tokenization, and locality self-attention modified Vision transformer showed an accuracy of 74.55% with expectations for further improvement in the future. Moreover, nowadays people have to choose a model from several other models to solve an issue, and as the model keeps on improving, it becomes very difficult to understand how the model works internally. So, for this reason, Explainable Artificial Intelligence (XAI) is introduced to give an idea of a human-readable explanation for the decision-making process of a model. This will certainly benefit cosmetologists, health researchers, research scientists, and researchers working in various areas and offer patients more convenience. Faiza Radiah Kabasum Rahman Lasania Asadullah Md. Sohanur Rahman Sohan Jaki Ahmed B.Sc. in Computer Science 2024-05-15T06:23:44Z 2024-05-15T06:23:44Z ©2023 2023-09 Thesis ID: 19101288 ID: 19101645 ID: 19101144 ID: 19301229 ID: 19301161 http://hdl.handle.net/10361/22837 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. 37 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Skin cancer detection Disease detection CNN Deep learning Dermatoscopy VGG16 XAI Artificial intelligence--Medical applications Neural networks (Computer science) Deep learning (Machine learning) |
| spellingShingle |
Skin cancer detection Disease detection CNN Deep learning Dermatoscopy VGG16 XAI Artificial intelligence--Medical applications Neural networks (Computer science) Deep learning (Machine learning) Radiah, Faiza Rahman, Kabasum Asadullah, Lasania Sohan, Md. Sohanur Rahman Ahmed, Jaki Explainable AI (XAI) driven skin cancer detection using transformer and CNN based architecture |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. |
| author2 |
Alam, Md. Ashraful |
| author_facet |
Alam, Md. Ashraful Radiah, Faiza Rahman, Kabasum Asadullah, Lasania Sohan, Md. Sohanur Rahman Ahmed, Jaki |
| format |
Thesis |
| author |
Radiah, Faiza Rahman, Kabasum Asadullah, Lasania Sohan, Md. Sohanur Rahman Ahmed, Jaki |
| author_sort |
Radiah, Faiza |
| title |
Explainable AI (XAI) driven skin cancer detection using transformer and CNN based architecture |
| title_short |
Explainable AI (XAI) driven skin cancer detection using transformer and CNN based architecture |
| title_full |
Explainable AI (XAI) driven skin cancer detection using transformer and CNN based architecture |
| title_fullStr |
Explainable AI (XAI) driven skin cancer detection using transformer and CNN based architecture |
| title_full_unstemmed |
Explainable AI (XAI) driven skin cancer detection using transformer and CNN based architecture |
| title_sort |
explainable ai (xai) driven skin cancer detection using transformer and cnn based architecture |
| publisher |
Brac University |
| publishDate |
2024 |
| url |
http://hdl.handle.net/10361/22837 |
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