Computer vision based skin disease detection using machine learning
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.
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| التنسيق: | أطروحة |
| اللغة: | English |
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Brac University
2023
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://hdl.handle.net/10361/21845 |
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10361-21845 |
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10361-218452023-10-16T21:05:55Z Computer vision based skin disease detection using machine learning Jayeb, Ahmad Wasiq Hore, Alvin Rahul Anjum, Ramisa Sadeque, Sohana Sanjana Auqib, Syed Tahsin Khondaker, Arnisha Department of Computer Science and Engineering, Brac University Image processing Deep learning Machine learning Attention Unet R2Unet ResUnet Dense layer Keras layer Keras TensorFlow Validation Accuracy U-net CNN KNN Skin cancer SoftMax Skin disease Pattern recognition Optical data processing This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019. Cataloged from PDF version of thesis. Includes bibliographical references (pages 61-63). Skin cancer have been the primary focus of this study, as they are one of the most deadly diseases if not diagnosed and treated early. The study will make it possible for computer science and medical science to work together to save lives. Machine Learning, Deep Learning, and Image Processing have already been used to treat skin conditions. Even though Deep Learning, Machine Learning, and Image Processing have all been used in the past to treat skin diseases, we are trying to improve the accuracy of this work by implementing new Image Processing and Machine Learning models.In this study, pre-trained CNN models and combined pre-trained Unet models are used to identify skin cancer and categorize the kind of cancer using datasets with labeled pictures and classes. Although it may develop in areas of your skin that are are not exposed to sunlight, skin cancer is an abnormal growth of skin cells that most frequently affects skin that has been exposed to the sun. Skin cancer is a kind of malignant melanoma, which is a type of cancer. The three most common kinds of skin cancer are melanoma, basal cell carcinoma, and squamous cell carcinoma. Approximately 301 people die from skin cancer each year. It will be difficult to achieve high accuracy if you rely just on the dataset provided by Kaggle. Recognize that not all datasets are balanced. Therefore, this study focuses on identifying several methods to combine Deep CNN models with U-net to produce deep CNN models that have the highest accuracy on both large and small datasets. These methods mostly depend on supervised learning, which involves the use labels and training data from datasets.This research aims to demonstrate how the best model can be used to accurately diagnose Skin cancer and disorders at an early stage. Here, we have classified a unique dataset using U-net model. We further examined the CNN, U-net, pre-trained models, and other Unet based models for example Attention Unet, ResUnet, R2Unet models accuracy to develop an optimum model that may be further customized to a mobile application for widespread usage. On more than 10000 photos of seven different skin conditions compared to healthy skin, we built the architecture. On the basis of the images of our researched dataset, we carefully compared our data and classified it. Finally, using ResUnet, which is the best model for the task at hand, we accurately identified the Seven illnesses with a 87.21% accuracy rate. Ahmad Wasiq Jayeb Alvin Rahul Hore Ramisa Anjum Sohana Sanjana Sadeque Syed Tahsin Auqib B.Sc. in Computer Science 2023-10-16T07:02:45Z 2023-10-16T07:02:45Z ©2022 2022-09-28 Thesis ID 18101057 ID 18101488 ID 18101612 ID 18101326 ID 18101206 http://hdl.handle.net/10361/21845 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. 73 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Image processing Deep learning Machine learning Attention Unet R2Unet ResUnet Dense layer Keras layer Keras TensorFlow Validation Accuracy U-net CNN KNN Skin cancer SoftMax Skin disease Pattern recognition Optical data processing |
| spellingShingle |
Image processing Deep learning Machine learning Attention Unet R2Unet ResUnet Dense layer Keras layer Keras TensorFlow Validation Accuracy U-net CNN KNN Skin cancer SoftMax Skin disease Pattern recognition Optical data processing Jayeb, Ahmad Wasiq Hore, Alvin Rahul Anjum, Ramisa Sadeque, Sohana Sanjana Auqib, Syed Tahsin Computer vision based skin disease detection using machine learning |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019. |
| author2 |
Khondaker, Arnisha |
| author_facet |
Khondaker, Arnisha Jayeb, Ahmad Wasiq Hore, Alvin Rahul Anjum, Ramisa Sadeque, Sohana Sanjana Auqib, Syed Tahsin |
| format |
Thesis |
| author |
Jayeb, Ahmad Wasiq Hore, Alvin Rahul Anjum, Ramisa Sadeque, Sohana Sanjana Auqib, Syed Tahsin |
| author_sort |
Jayeb, Ahmad Wasiq |
| title |
Computer vision based skin disease detection using machine learning |
| title_short |
Computer vision based skin disease detection using machine learning |
| title_full |
Computer vision based skin disease detection using machine learning |
| title_fullStr |
Computer vision based skin disease detection using machine learning |
| title_full_unstemmed |
Computer vision based skin disease detection using machine learning |
| title_sort |
computer vision based skin disease detection using machine learning |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/21845 |
| work_keys_str_mv |
AT jayebahmadwasiq computervisionbasedskindiseasedetectionusingmachinelearning AT horealvinrahul computervisionbasedskindiseasedetectionusingmachinelearning AT anjumramisa computervisionbasedskindiseasedetectionusingmachinelearning AT sadequesohanasanjana computervisionbasedskindiseasedetectionusingmachinelearning AT auqibsyedtahsin computervisionbasedskindiseasedetectionusingmachinelearning |
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1814307014643286016 |