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Titlebook: Biomedical Signal and Image Processing with Artificial Intelligence; Chirag Paunwala,Mita Paunwala,Norliza Mohd. Noor Book 2023 The Editor

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發(fā)表于 2025-3-21 17:36:57 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Biomedical Signal and Image Processing with Artificial Intelligence
影響因子2023Chirag Paunwala,Mita Paunwala,Norliza Mohd. Noor
視頻videohttp://file.papertrans.cn/189/188095/188095.mp4
發(fā)行地址Provides insights into medical signal and image analysis using artificial intelligence.Includes novel and recent trends of decision support system for medical research.Outlines employment of evolution
學(xué)科分類EAI/Springer Innovations in Communication and Computing
圖書封面Titlebook: Biomedical Signal and Image Processing with Artificial Intelligence;  Chirag Paunwala,Mita Paunwala,Norliza Mohd. Noor Book 2023 The Editor
影響因子.This book focuses on advanced techniques used for feature extraction, analysis, recognition, and classification in the area of biomedical signal and image processing. Contributions cover all aspects of artificial intelligence, machine learning, and deep learning in the field of biomedical signal and image processing using novel and unexplored techniques and methodologies. The book covers recent developments in both medical images and signals analyzed by artificial intelligence techniques. The authors also cover topics related to development based artificial intelligence, which includes machine learning, neural networks, and deep learning. This book will provide a platform for researchers who are working in the area of artificial intelligence for biomedical applications..Provides insights into medical signal and image analysis using artificial intelligence;.Includes novel and recent trends of decision support system for medical research;.Outlines employment of evolutionary algorithms for biomedical data, big data analysis for medical databases, and reliability, opportunities, and challenges in clinical data.. .
Pindex Book 2023
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Histopathology Whole Slide Image Analysis for Breast Cancer Detection,etection task. Some of the salient aspects of our approach include extracting embeddings from a pretrained CNN network, using a cosine loss and training schedule for the classification network, and suggesting an overall decision making criteria for the WSI based on intermediate decisions on local ra
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An Object Aware Hybrid U-Net for Breast Tumour Annotation,und the suspected tumour region. The polygonal boundary covers the extent of the tumour in the slide. However, this kind of annotation is time consuming and tedious when there are large datasets..Objective: In this proposed work, we have tried to imitate pathologist like annotation by segmenting tum
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Content-Based Image Retrieval Techniques and Their Applications in Medical Science,ue to the massive number of images generated by the mobiles and various image-capturing machines..Objectives: Any image retrieval system’s primary goal is to reduce the semantic gap between low-level features and high-level perception..Methods: The CBIR techniques are classified into multiple catego
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Data Analytics on Medical Images with Deep Learning Approach, also foster scientific and technological advances. Medical images play a significant role in diagnosis and experimentation. For years, many experimentations and analyses have been made on medical images with a broad scope of achieving the desired results. Medical modalities like MRI, CT, PET, X-ray
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