Skin disease detection and classification using deep learning
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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| 语言: | English |
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Brac University
2022
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| 在线阅读: | http://hdl.handle.net/10361/17027 |
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10361-17027 |
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10361-170272022-07-24T21:01:32Z Skin disease detection and classification using deep learning Shuvon, Mehedi Hasan Sadia, Rowshanara Shormi, Shanjida Habib Arafin, Umma Tania Chowdhury, Md. Rawha Mikdad Rhaman, Md. Khalilur Department of Computer Science and Engineering, Brac University Image processing Deep learning MobileNetV2 InceptionV3 ResNetV2 Epoch Softmax Skin disease KNN CNN Detection Tensorflow Keras Layer Dense layer Machine learning Image processing -- Digital techniques. Cognitive learning theory (Deep learning) 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 36-37). Skin Diseases have been the primary focus of this study, as they are one of the most lethal diseases if not diagnosed and treated early. The research will enable the fields of Medical Science and Computer Science to collaborate in order to save lives. Although Machine Learning, Deep Learning, and Image Processing have been utilized previously to treat skin diseases, we are attempting to improve the accuracy of this work by implementing new models of Image Processing and Deep Learning. The purpose of this research is to demonstrate how to accurately diagnose Skin diseases at an early stage using the optimum model. Here we have used three distinct neural models to classify a custom dataset. We further analyzed the accuracy of the MobileNetV2, InceptionV3, and ResNetV2 to come up with an optimized model that can be configured further to a mobile application for vast use. We built the architecture on more than 1450 images representing nine distinct skin disorders in comparison with fresh skin. We carefully compared our data and classified it based on the images of our customized dataset. Finally, we determined the nine diseases with a 96.77% accuracy with the help of MobileNetV2 which is our ideal model for the goal we want to achieve. Mehedi Hasan Shuvon Rowshanara Sadia Shanjida Habib Shormi Umma Tania Arafin Md. Rawha Mikdad Chowdhury B. Computer Science 2022-07-24T06:37:06Z 2022-07-24T06:37:06Z 2022 2022-01 Thesis ID 18101686 ID 18101188 ID 18101097 ID 18201203 ID 18101672 http://hdl.handle.net/10361/17027 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 |
Image processing Deep learning MobileNetV2 InceptionV3 ResNetV2 Epoch Softmax Skin disease KNN CNN Detection Tensorflow Keras Layer Dense layer Machine learning Image processing -- Digital techniques. Cognitive learning theory (Deep learning) |
| spellingShingle |
Image processing Deep learning MobileNetV2 InceptionV3 ResNetV2 Epoch Softmax Skin disease KNN CNN Detection Tensorflow Keras Layer Dense layer Machine learning Image processing -- Digital techniques. Cognitive learning theory (Deep learning) Shuvon, Mehedi Hasan Sadia, Rowshanara Shormi, Shanjida Habib Arafin, Umma Tania Chowdhury, Md. Rawha Mikdad Skin disease detection and classification using deep learning |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. |
| author2 |
Rhaman, Md. Khalilur |
| author_facet |
Rhaman, Md. Khalilur Shuvon, Mehedi Hasan Sadia, Rowshanara Shormi, Shanjida Habib Arafin, Umma Tania Chowdhury, Md. Rawha Mikdad |
| format |
Thesis |
| author |
Shuvon, Mehedi Hasan Sadia, Rowshanara Shormi, Shanjida Habib Arafin, Umma Tania Chowdhury, Md. Rawha Mikdad |
| author_sort |
Shuvon, Mehedi Hasan |
| title |
Skin disease detection and classification using deep learning |
| title_short |
Skin disease detection and classification using deep learning |
| title_full |
Skin disease detection and classification using deep learning |
| title_fullStr |
Skin disease detection and classification using deep learning |
| title_full_unstemmed |
Skin disease detection and classification using deep learning |
| title_sort |
skin disease detection and classification using deep learning |
| publisher |
Brac University |
| publishDate |
2022 |
| url |
http://hdl.handle.net/10361/17027 |
| work_keys_str_mv |
AT shuvonmehedihasan skindiseasedetectionandclassificationusingdeeplearning AT sadiarowshanara skindiseasedetectionandclassificationusingdeeplearning AT shormishanjidahabib skindiseasedetectionandclassificationusingdeeplearning AT arafinummatania skindiseasedetectionandclassificationusingdeeplearning AT chowdhurymdrawhamikdad skindiseasedetectionandclassificationusingdeeplearning |
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1814308328433516544 |