EAI4CC: deciphering lung and colon cancer categorization within a federated learning framework harnessing the power of explainable artificial intelligence

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.

ग्रंथसूची विवरण
मुख्य लेखकों:	Mim, Ankhi Akter, Ashakin, Kazi Habibul, Hossain, Sadat, Orchi, Nabiha Tasnim, Him, Al Shahriar
अन्य लेखक:	Alam, Md. Ashraful
स्वरूप:	थीसिस
भाषा:	English
प्रकाशित:	Brac University 2024
विषय:	CNN XAI AI DenseNet121 ResNet50 Federated learning GradCAM VGG16 Histopathological image Deep learning (Machine learning) Artificial intelligence > Medical applications Expert systems (Computer science)
ऑनलाइन पहुंच:	http://hdl.handle.net/10361/22879

समान संसाधन

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Early stage detection and classification of colon cancer using deep learning and explainable AI on histopathological images
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Myocardial infarction detection using ECG signal applying deep learning techniques - ConvNet, VGG16, InceptionV3 and MobileNet
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A comparison of deep learning U‐Net architectures for semantic segmentation on panoramic X-ray images
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An efficient deep learning approach to detect retinal disease using optical coherence tomographic images
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Enhancing Monkeypox diagnosis: a machine learning approach for skin lession classification
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Tomato leaf disease detection using Resnet-50 and MobileNet Architecture
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Image segmentation of X-Ray and optical images using U-Net/UNet++ based deep learning architecture
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A deep learning approach for pneumonia classiﬁcation from chest X-Ray images with ensemble modelling and explainable AI
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Skin disease detection and classification using deep learning
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