An enhanced CNN model for classifying skin cancer
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022
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Brac University
2022
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| Online dostop: | http://hdl.handle.net/10361/17131 |
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10361-171312022-08-29T21:01:35Z An enhanced CNN model for classifying skin cancer Haider, Kazi MD Minhajul Dhar, Mondira Akter, Fahima Islam, Sadia Shariar, Syed Ragib Hossain, Muhammad Iqbal Mostakim, Moin Department of Computer Science and Engineering, Brac University Skin cancer CNN Deep learning Medical imaging Accuracy Neural networks (Computer science) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022 Cataloged from PDF version of thesis. Includes bibliographical references (pages 41-42). Unrepaired deoxyribonucleic acid in skin cells causes skin cancer by generating genetic abnormalities or mutations, rising day by day. Detecting and diagnosing skin cancer in its early stages is expensive and challenging, giving superior treatment options. Given the severity of these issues, researchers have generated a set of early classification techniques for skin cancer. Skin cancer is diagnosed and segregated from melanoma by looking at the symmetry, color, size, shape, and other features of lesions. While there are various computerized approaches for classifying skin lesions, convolutional neural networks (CNNs) have been demonstrated to exceed standard practices. Moreover, CNNs are a type of deep learning that has been prominent in various fields, including medical imaging. Multiple machine learning libraries have been used in this paper. Also, we have used five pre-trained models such as Inception V3, VGG-19, VGG-16, Efficient Net B7, ResNet 50 models and presented our proposed model for skin cancer classification using the HAM10000 dataset, which is an enormous skin cancer dataset. Following that, each competent model’s image detection categorization accuracy is evaluated by comparing and assessing. This research reports a maximum accuracy of 85.25% for Inception V3 models within five pre-trained models and maximum accuracy of 90.55% for our proposed model. In terms of image detection, our experimental configuration shows that our proposed model can attain the best classification accuracy rather than the other five pretrained models. Our findings are helpful in providing a comprehensive comparison and analysis of many neural networks in the categorization of skins cancer. Kazi MD Minhajul Haider Mondira Dhar Fahima Akter Sadia Islam Syed Ragib Shahriar B. Computer Science 2022-08-29T08:38:29Z 2022-08-29T08:38:29Z 2022 2022-01 Thesis ID 18101708 ID 21241070 ID 19101642 ID 18301232 ID 18101571 http://hdl.handle.net/10361/17131 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. 42 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Skin cancer CNN Deep learning Medical imaging Accuracy Neural networks (Computer science) |
| spellingShingle |
Skin cancer CNN Deep learning Medical imaging Accuracy Neural networks (Computer science) Haider, Kazi MD Minhajul Dhar, Mondira Akter, Fahima Islam, Sadia Shariar, Syed Ragib An enhanced CNN model for classifying skin cancer |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022 |
| author2 |
Hossain, Muhammad Iqbal |
| author_facet |
Hossain, Muhammad Iqbal Haider, Kazi MD Minhajul Dhar, Mondira Akter, Fahima Islam, Sadia Shariar, Syed Ragib |
| format |
Thesis |
| author |
Haider, Kazi MD Minhajul Dhar, Mondira Akter, Fahima Islam, Sadia Shariar, Syed Ragib |
| author_sort |
Haider, Kazi MD Minhajul |
| title |
An enhanced CNN model for classifying skin cancer |
| title_short |
An enhanced CNN model for classifying skin cancer |
| title_full |
An enhanced CNN model for classifying skin cancer |
| title_fullStr |
An enhanced CNN model for classifying skin cancer |
| title_full_unstemmed |
An enhanced CNN model for classifying skin cancer |
| title_sort |
enhanced cnn model for classifying skin cancer |
| publisher |
Brac University |
| publishDate |
2022 |
| url |
http://hdl.handle.net/10361/17131 |
| work_keys_str_mv |
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