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Deep learning and computer visionmodels and biomedical applications.Volume 1 /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep learning and computer visionedited by Uma N. Dulhare, Essam Halim Houssein.
其他題名:	models and biomedical applications.
其他作者:	Dulhare, Uma N.
出版者:	Singapore :Springer Nature Singapore :2025.
面頁冊數:	xiii, 216 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Deep learning (Machine learning)
電子資源:	https://doi.org/10.1007/978-981-96-1285-7
ISBN:	9789819612857$q(electronic bk.)

Deep learning and computer visionmodels and biomedical applications.Volume 1 /
Deep learning and computer visionmodels and biomedical applications.Volume 1 /[electronic resource] :edited by Uma N. Dulhare, Essam Halim Houssein. - Singapore :Springer Nature Singapore :2025. - xiii, 216 p. :ill. (some col.), digital ;24 cm. - Algorithms for intelligent systems,2524-7573. - Algorithms for intelligent systems..

Computer-Aided Diagnosis System for Liver Fibrosis Using Data Mining Techniques -- Deep learning for sequence alignment -- Protein Structure Prediction: A Computational Approach to Unravelling Molecular Mysteries -- Management of cancer-associated thrombosis and related complications -- Integrating Machine Vision for Enhanced Biomedical Signal and Image Processing -- Diagnostic strategies using AI and ML in cardiovascular diseases: Challenges and Future Perspectives -- Analytics of medical data using Cognos -- Analytics of Medical Data -- Integrating Deep Learning into Electronic Health Records: Opportunities and Challenges -- Heart disease prediction using machine learning algorithms and quantum variational classifier -- Deep Learning Based Approaches for Early Detection of Parkinson's Disease -- Exploring Recent Developments in Radiographic Chest Disease Detection through Deep Learning Models -- Nano encapsulation for the targeted drug delivery to enhance the efficacy of drugs -- Early Detection of Brain Tumor Automation System using Hybrid SMOTE ENN And Deep Convolution Neural Network Technique -- Advancements in Medical Device Integration Technology and its Impact on Healthcare -- Image Classification To Detect Breast Cancer Using Transfer Learning -- Medical Computer Vision -- Machine Vision & Biomedical Signal and Image Processing -- Metaheuristic Algorithms for Solving Various Optimization Problems: Comprehensive Review -- Maximizing Renewable Energy: Harnessing the Power of Metaheuristic Optimization Techniques -- Review on occurrence of skin lesions due to increased ultra-violet rays for diagnosis of skin cancer to sustain life using deep learning model -- Early Detection of PCOD Automation System using Deep Convolution Neural Network Technique -- Design of Nano Magnetorheological fluid damper - based Leg prosthesis for Amputees -- Learning Analytics in Higher Education: Promises and Challenges -- Neural Network Fusion for Forgery Detection in Digital Images.

This book takes a balanced approach between theoretical understanding and real time applications. All topics show how to explore, build, evaluate and optimize deep learning models with computer vision. Deep learning is integrated with computer vision to enhance the performance of image classification with localization, object detection, object recognition, object segmentation, image style transfer, image colorization, image reconstruction, image super-resolution, image synthesis, motion detection, pose estimation, semantic segmentation in biomedical field. Huge number of efficient approaches/applications and models support medical decisions in the fields of cardiology, dermatology, and radiology. The content of book elaborates deep learning models such as convolution neural networks, deep learning, generative adversarial network, long short-term memory networks (LSTM), autoencoder (AE), restricted Boltzmann machine (RBM), self-organizing map (SOM), deep belief network (DBN), etc.

ISBN: 9789819612857$q(electronic bk.)

Standard No.: 10.1007/978-981-96-1285-7doiSubjects--Topical Terms:

913129
Deep learning (Machine learning)

LC Class. No.: Q325.73

Dewey Class. No.: 006.31

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