Multi-classification Network for Detecting Skin Diseases using Deep Learning and XAI
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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| বিন্যাস: | গবেষণাপত্র |
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Brac University
2022
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| অনলাইন ব্যবহার করুন: | http://hdl.handle.net/10361/17634 |
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10361-17634 |
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10361-176342022-12-12T21:01:42Z Multi-classification Network for Detecting Skin Diseases using Deep Learning and XAI Athina, Fahima Hasan Sara, Sadaf Ahmed Tabassum, Nishat Sarwar, Quazi Sabrina Jannat Era, Mun Tarin Hossain, Dr. Muhammad Iqbal Bin Ashraf, Faisal Department of Computer Science and Engineering, Brac University Skin Disease Deep Learning ResNet50V2 Inceptionv3 InceptionResNetV2 XAI Machine Learning Computer algorithms 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 53-55). This research work aims to show a comparative analysis among four different deep learning approaches to classify three rare but deadly skin diseases namely Stevens Johnson Syndrome, Erythema Multiforme and Bullous Pemphigoid. As the features of these diseases often overlap with each other, it becomes challenging for physicians to distinguish them with their naked eye. Thus, this research work is initiated to find a model that provides an efficient way to identify them for preventing misdiagnosis. This work also attempts to interpret the prediction of these models using LIME based Explainable Artificial Intelligence (XAI). Here, the four pre-trained models namely ResNet50V2, VGG16, Inceptionv3 and InceptionRes NetV2 have been used for feature extraction. The top layer of these models have been replaced with a customized 10-layer architecture consisting of Convolution, BatchNormalization, Dropout and Dense Layers. These models have been trained on a hybrid dataset comprising of colored images of the diseases collected from dif ferent sources. Moreover, different machine learning classification algorithms (i.e. Random Forest, Logistic Regression, and Support Vector Machine) have been used to classify the images to see how well they perform compared to a neural network approach. Lastly, the accuracy of the attempted models have been compared with each other to identify which algorithm shows the best performance. The analysis shows that the InceptionResnetV2 model provides the highest accuracy of 99.06% while InceptionV3, VGG16 and Resnet50V2 provide 90.27%, 95.92% and 98.26% respectively. Fahima Hasan Athina Sadaf Ahmed Sara Nishat Tabassum Quazi Sabrina Sarwar Mun Tarin Jannat Era B. Computer Science and Engineering 2022-12-12T08:25:54Z 2022-12-12T08:25:54Z 2022 2022-05 Thesis ID: 18101234 ID: 18101284 ID: 18101281 ID: 19101666 ID: 18101245 http://hdl.handle.net/10361/17634 en_US 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. 55 Pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
en_US |
| topic |
Skin Disease Deep Learning ResNet50V2 Inceptionv3 InceptionResNetV2 XAI Machine Learning Computer algorithms Cognitive learning theory (Deep learning) |
| spellingShingle |
Skin Disease Deep Learning ResNet50V2 Inceptionv3 InceptionResNetV2 XAI Machine Learning Computer algorithms Cognitive learning theory (Deep learning) Athina, Fahima Hasan Sara, Sadaf Ahmed Tabassum, Nishat Sarwar, Quazi Sabrina Jannat Era, Mun Tarin Multi-classification Network for Detecting Skin Diseases using Deep Learning and XAI |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. |
| author2 |
Hossain, Dr. Muhammad Iqbal |
| author_facet |
Hossain, Dr. Muhammad Iqbal Athina, Fahima Hasan Sara, Sadaf Ahmed Tabassum, Nishat Sarwar, Quazi Sabrina Jannat Era, Mun Tarin |
| format |
Thesis |
| author |
Athina, Fahima Hasan Sara, Sadaf Ahmed Tabassum, Nishat Sarwar, Quazi Sabrina Jannat Era, Mun Tarin |
| author_sort |
Athina, Fahima Hasan |
| title |
Multi-classification Network for Detecting Skin Diseases using Deep Learning and XAI |
| title_short |
Multi-classification Network for Detecting Skin Diseases using Deep Learning and XAI |
| title_full |
Multi-classification Network for Detecting Skin Diseases using Deep Learning and XAI |
| title_fullStr |
Multi-classification Network for Detecting Skin Diseases using Deep Learning and XAI |
| title_full_unstemmed |
Multi-classification Network for Detecting Skin Diseases using Deep Learning and XAI |
| title_sort |
multi-classification network for detecting skin diseases using deep learning and xai |
| publisher |
Brac University |
| publishDate |
2022 |
| url |
http://hdl.handle.net/10361/17634 |
| work_keys_str_mv |
AT athinafahimahasan multiclassificationnetworkfordetectingskindiseasesusingdeeplearningandxai AT sarasadafahmed multiclassificationnetworkfordetectingskindiseasesusingdeeplearningandxai AT tabassumnishat multiclassificationnetworkfordetectingskindiseasesusingdeeplearningandxai AT sarwarquazisabrina multiclassificationnetworkfordetectingskindiseasesusingdeeplearningandxai AT jannateramuntarin multiclassificationnetworkfordetectingskindiseasesusingdeeplearningandxai |
| _version_ |
1814307276846006272 |