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Titlebook: Artificial Intelligence on Medical Data; Proceedings of Inter Mousumi Gupta,Sujata Ghatak,Abir Lal Mukherjee Conference proceedings 2023 Th

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樓主: GUST
51#
發(fā)表于 2025-3-30 11:54:00 | 只看該作者
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發(fā)表于 2025-3-30 13:05:58 | 只看該作者
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發(fā)表于 2025-3-30 20:06:54 | 只看該作者
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發(fā)表于 2025-3-30 22:55:08 | 只看該作者
Artificial Intelligence on Medical Data978-981-19-0151-5Series ISSN 2212-9391 Series E-ISSN 2212-9413
55#
發(fā)表于 2025-3-31 03:51:52 | 只看該作者
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發(fā)表于 2025-3-31 05:25:14 | 只看該作者
Analysis of Machine Learning and Deep Learning Algorithms for Detection of Brain Disorders Using MRI algorithms such as support vector machine (SVM), Decision Tree, Random Forest, Gaussian Naive Bayes, and 1D-CNN deep learning algorithm have been used for the classification of the disorders. It has been observed that Gaussian NB performed the best on Schizophrenia data, while Random Forest outperf
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發(fā)表于 2025-3-31 11:48:54 | 只看該作者
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發(fā)表于 2025-3-31 16:49:32 | 只看該作者
Imaging Radiomic-Driven Framework for Automated Cancer Investigation terms of quantitative analysis. The biomedical tools are not suitable for extracting the quantitative data. When the gliomas tumor is in images, then radiomics can be used to extract valuable quantitative information for diagnosis and prediction of gliomas tumor. Automated feature learning from med
59#
發(fā)表于 2025-3-31 20:42:54 | 只看該作者
Neoplastic—Nuclei Cell Labelling Using Mask R-CNNoposed technique generates a pixel-wise binary mask. These masks are capable of segmenting these instances and facilitating the advancement of intelligent systems in medical imaging and computational pathology. This time can instead be devoted to developing better cures by conducting more research.
60#
發(fā)表于 2025-4-1 01:08:51 | 只看該作者
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