Skin lesion classification using different CNN models
This project report is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2023.
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| Lenguaje: | English |
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Brac University
2023
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| Acceso en línea: | http://hdl.handle.net/10361/21229 |
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10361-212292023-09-25T21:06:07Z Skin lesion classification using different CNN models Oli, Md. Yahea Sultan Chakrabarty, Amitabha Department of Computer Science and Engineering, Brac University Cancer Distinguished Melanoma Lesion Densenet121 VGG16 In- ceptionv3 ResNet50 Computer algorithms Pattern recognition systems Neural networks (Computer science) This project report is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2023. Cataloged from PDF version of the project report. Includes bibliographical references (pages 38-40). The field of dermatoscopic image classification has gained significant attention as there is a growing demand for early diagnosis of specific diseases. The use of deep learning is increasingly significant in the quest for a more effective dermoscopic anal- ysis method. The “HAM10000” (Human Against Machine) dataset has been used in this study for classification of 7 different types of skin lesions by using DenseNet-121, VGG16, ResNet50, and Inceptionv3 model. To improve the classifier’s performance, data augmentation was applied. This study could help dermatologists in the clinic make more precise decisions when identifying skin lesions, which would be benefi- cial. With this project I have tried to improve the model so that dermatologists identify skin lesions more precisely. Through the implementation of data augmen- tation techniques, this project achieved an impressive categorical accuracy of 92% and a top2 accuracy of 97% using DenseNet-121. The remaining models, VGG16, ResNet50, Inceptionv3 achieved accuracy 80%, 78%, 84% respectively. This project could have a beneficial impact on dermatoscopic image recognition and can reduce time and valuable resources. It can also help to saves life where robust diagnosing is not available. Md. Yahea Sultan Oli M. Computer Science and Engineering 2023-09-25T06:35:21Z 2023-09-25T06:35:21Z 2023 2023-05 Project report ID 19166012 http://hdl.handle.net/10361/21229 en Brac University project reports 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. 50 pages application/pdf Brac University |
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Brac University |
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Institutional Repository |
| language |
English |
| topic |
Cancer Distinguished Melanoma Lesion Densenet121 VGG16 In- ceptionv3 ResNet50 Computer algorithms Pattern recognition systems Neural networks (Computer science) |
| spellingShingle |
Cancer Distinguished Melanoma Lesion Densenet121 VGG16 In- ceptionv3 ResNet50 Computer algorithms Pattern recognition systems Neural networks (Computer science) Oli, Md. Yahea Sultan Skin lesion classification using different CNN models |
| description |
This project report is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2023. |
| author2 |
Chakrabarty, Amitabha |
| author_facet |
Chakrabarty, Amitabha Oli, Md. Yahea Sultan |
| format |
Project report |
| author |
Oli, Md. Yahea Sultan |
| author_sort |
Oli, Md. Yahea Sultan |
| title |
Skin lesion classification using different CNN models |
| title_short |
Skin lesion classification using different CNN models |
| title_full |
Skin lesion classification using different CNN models |
| title_fullStr |
Skin lesion classification using different CNN models |
| title_full_unstemmed |
Skin lesion classification using different CNN models |
| title_sort |
skin lesion classification using different cnn models |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/21229 |
| work_keys_str_mv |
AT olimdyaheasultan skinlesionclassificationusingdifferentcnnmodels |
| _version_ |
1814308710095257600 |