A decentralized learning-based approach to classify colorectal cancer using Deep Learning Leveraging 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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Brac University
2022
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| Acceso en línea: | http://hdl.handle.net/10361/17630 |
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10361-176302022-12-12T21:01:38Z A decentralized learning-based approach to classify colorectal cancer using Deep Learning Leveraging XAI Mubin, Kazi Ehsanul Arthi, Noshin Tabassum Rahman, Junayed Rafi, G. M. Sheja, Tahsina Tanzim Alam, Md. Ashraful Reza, Md Tanzim Department of Computer Science and Engineering, Brac University Federated Learning XAI Deep Learning Colorectal Cancer Convolutional Neural Network Image Classification ResNeXt50 Artificial intelligence Machine 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). Convolutional Neural Networks (CNN)-based automated approaches are vastly utilised to anticipate and diagnose cancer, saving time and reducing mistakes. Deep Learning (DL) CNN methods use a variety of probabilistic and statistical methodologies to make com puters understand and identify patterns in datasets based on previous experiences. We proposed an efficient federated learning based model to classify histopathological images for detecting colorectal cancer while providing high prediction accuracy and maintaining data privacy. Federated learning solves the problem of retaining privacy while utilizing vast and heterogeneous private datasets collected from numerous healthcare facilities. As the amount of patient data obtained for the process of machine learning is significantly responsible for the success of enhancing the accuracy of the system, the experiment was performed on a large dataset including cancerous and non-cancerous colorectal tissue im ages. FL can also mitigate costs resulting from traditional, centralized machine learning approaches. We have also applied the XAI method, a model-agnostic approach to acquire an explicit demonstration of the applied machine learning models. With XAI, we can visualize the super pixels of our colorectal tissue images through accepting and reject ing features. While applying various CNN models such as VGG16 & 19, InceptionV3, ResNet50, ResNeXt50, and comparing their precision, ResNeXt50 was established with the highest accuracy of 99.53%. Therefore, we have applied ResNeXt50 on FL that brings forth the accuracy of 96.045% and F1 Score is 0.96. Kazi Ehsanul Mubin Noshin Tabassum Arthi Junayed Rahman G. M. Raf Tahsina Tanzim Sheja B. Computer Science and Engineering 2022-12-12T05:35:45Z 2022-12-12T05:35:45Z 2022 2022-05 Thesis ID: 18101391 ID: 18101100 ID: 18101095 ID: 18101465 ID: 18101504 http://hdl.handle.net/10361/17630 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 |
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Brac University |
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Institutional Repository |
| language |
en_US |
| topic |
Federated Learning XAI Deep Learning Colorectal Cancer Convolutional Neural Network Image Classification ResNeXt50 Artificial intelligence Machine learning |
| spellingShingle |
Federated Learning XAI Deep Learning Colorectal Cancer Convolutional Neural Network Image Classification ResNeXt50 Artificial intelligence Machine learning Mubin, Kazi Ehsanul Arthi, Noshin Tabassum Rahman, Junayed Rafi, G. M. Sheja, Tahsina Tanzim A decentralized learning-based approach to classify colorectal cancer using Deep Learning Leveraging XAI |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. |
| author2 |
Alam, Md. Ashraful |
| author_facet |
Alam, Md. Ashraful Mubin, Kazi Ehsanul Arthi, Noshin Tabassum Rahman, Junayed Rafi, G. M. Sheja, Tahsina Tanzim |
| format |
Thesis |
| author |
Mubin, Kazi Ehsanul Arthi, Noshin Tabassum Rahman, Junayed Rafi, G. M. Sheja, Tahsina Tanzim |
| author_sort |
Mubin, Kazi Ehsanul |
| title |
A decentralized learning-based approach to classify colorectal cancer using Deep Learning Leveraging XAI |
| title_short |
A decentralized learning-based approach to classify colorectal cancer using Deep Learning Leveraging XAI |
| title_full |
A decentralized learning-based approach to classify colorectal cancer using Deep Learning Leveraging XAI |
| title_fullStr |
A decentralized learning-based approach to classify colorectal cancer using Deep Learning Leveraging XAI |
| title_full_unstemmed |
A decentralized learning-based approach to classify colorectal cancer using Deep Learning Leveraging XAI |
| title_sort |
decentralized learning-based approach to classify colorectal cancer using deep learning leveraging xai |
| publisher |
Brac University |
| publishDate |
2022 |
| url |
http://hdl.handle.net/10361/17630 |
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