EAI4CC: deciphering lung and colon cancer categorization within a federated learning framework harnessing the power of explainable artificial intelligence
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.
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| التنسيق: | أطروحة |
| اللغة: | English |
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Brac University
2024
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://hdl.handle.net/10361/22879 |
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10361-22879 |
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10361-228792024-05-20T21:01:05Z EAI4CC: deciphering lung and colon cancer categorization within a federated learning framework harnessing the power of explainable artificial intelligence Mim, Ankhi Akter Ashakin, Kazi Habibul Hossain, Sadat Orchi, Nabiha Tasnim Him, Al Shahriar Alam, Md. Ashraful Rahman, Rafeed Department of Computer Science and Engineering, Brac University CNN XAI AI DenseNet121 ResNet50 Federated learning GradCAM VGG16 Histopathological image Deep learning (Machine learning) Artificial intelligence--Medical applications Expert systems (Computer science) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024. Cataloged from PDF version of thesis. Includes bibliographical references (pages 53-56). Advances between medical imaging and artificial intelligence (AI) have led to improvements in cancer diagnosis and classification. This paper provides a new framework called Explainable AI for cancer categorization (EAI4CC), which has been developed to define lung and colorectal cancer classification in an integrated manner, addressing privacy concerns by enabling collaborative model training using Federated Learning. In this study, EAI4CC used convolutional neural networks (CNNs) such as VGG 16, VGG19, ResNet50, DenseNet121 and Vision Transformer to analyze histopathological images from lung and colon tissue. In Federated Learning architecture it ensures data privacy while enabling model training on dispersed dataset. Furthermore, state-of-the-art artificial intelligence (XAI) presentation techniques are used. In particular gradient-weighted class activation mapping (GradCAM) combined with EAI4CC to elucidate the decision-making process of the model. The evaluation system shows good performance in important evaluation measures such as accuracy, precision, specificity, sensitivity, and F1 score. More importantly, it enhances model interpretation capabilities, explaining each prediction. This gives doctors clarity and confidence in AI-assisted diagnosis. Interpretable and reliable methods allow AI technologies to be responsibly integrated into the critical cancer research workflow to demonstrate the performance of model measures. In summary, this breakthrough sets a standard to establish a framework for AI to achieve more accurate, transparent, and equitable clinical decision-making. Ankhi Akter Mim Kazi Habibul Ashakin Sadat Hossain Nabiha Tasnim Orchi Al Shahriar Him B.Sc in Computer Science 2024-05-20T03:31:34Z 2024-05-20T03:31:34Z ©2024 2024-01 Thesis ID: 20101365 ID: 20101376 ID: 20101367 ID: 20301148 ID: 20301131 http://hdl.handle.net/10361/22879 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 |
CNN XAI AI DenseNet121 ResNet50 Federated learning GradCAM VGG16 Histopathological image Deep learning (Machine learning) Artificial intelligence--Medical applications Expert systems (Computer science) |
| spellingShingle |
CNN XAI AI DenseNet121 ResNet50 Federated learning GradCAM VGG16 Histopathological image Deep learning (Machine learning) Artificial intelligence--Medical applications Expert systems (Computer science) Mim, Ankhi Akter Ashakin, Kazi Habibul Hossain, Sadat Orchi, Nabiha Tasnim Him, Al Shahriar EAI4CC: deciphering lung and colon cancer categorization within a federated learning framework harnessing the power of explainable artificial intelligence |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024. |
| author2 |
Alam, Md. Ashraful |
| author_facet |
Alam, Md. Ashraful Mim, Ankhi Akter Ashakin, Kazi Habibul Hossain, Sadat Orchi, Nabiha Tasnim Him, Al Shahriar |
| format |
Thesis |
| author |
Mim, Ankhi Akter Ashakin, Kazi Habibul Hossain, Sadat Orchi, Nabiha Tasnim Him, Al Shahriar |
| author_sort |
Mim, Ankhi Akter |
| title |
EAI4CC: deciphering lung and colon cancer categorization within a federated learning framework harnessing the power of explainable artificial intelligence |
| title_short |
EAI4CC: deciphering lung and colon cancer categorization within a federated learning framework harnessing the power of explainable artificial intelligence |
| title_full |
EAI4CC: deciphering lung and colon cancer categorization within a federated learning framework harnessing the power of explainable artificial intelligence |
| title_fullStr |
EAI4CC: deciphering lung and colon cancer categorization within a federated learning framework harnessing the power of explainable artificial intelligence |
| title_full_unstemmed |
EAI4CC: deciphering lung and colon cancer categorization within a federated learning framework harnessing the power of explainable artificial intelligence |
| title_sort |
eai4cc: deciphering lung and colon cancer categorization within a federated learning framework harnessing the power of explainable artificial intelligence |
| publisher |
Brac University |
| publishDate |
2024 |
| url |
http://hdl.handle.net/10361/22879 |
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