Metaheuristic - Based Deep Learning for Medical Image Segmentation

Language: English

Published by Omniscriptum, LAP Lambert Academic Publishing, 2025

620844179X / 9786208441791

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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.

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This item is printed on demand - it takes 3-4 days longer - Neuware -Image segmentation is a crucial aspect of clinical decision-making in the medical field. The integration of image segmentation techniques has dramatically enhanced healthcare delivery. Also, the advancement of deep learning, particularly Convolutional Neural Networks (CNNs), has brought about a significant transformation in medical image analysis. These advanced algorithms have shown exceptional abilities in identifying complex patterns and features in medical images, revolutionizing diagnostic imaging. However, the complexity and scale of these models present significant challenges. This requires a substantial amount of computational resources and expert knowledge for successful implementation. Addressing these challenges is crucial to fully exploit the potential of deep learning in the field of medical image segmentation. To address the challenges, this study combines metaheuristic optimization algorithms with deep learning. These algorithms, inspired by natural processes, provide an effective way to optimize the structure and parameters of CNNs, thus making the process of medical image segmentation more efficient. 132 pp. Englisch.

Seller Inventory # 9786208441791

Title
Metaheuristic - Based Deep Learning for Medical Image Segmentation
Author
Mohammed Khouy
Publisher
Omniscriptum, LAP Lambert Academic Publishing
Publication year
2025
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
620844179X
ISBN 13
9786208441791
Dimensions
220x150x33 mm

BuchWeltWeit Ludwig Meier e.K.

Bergisch Gladbach, Germany

5-star seller

AbeBooks seller since January 11, 2012

Shipping rates from Germany to U.S.A.

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First itemUS$ 26.37US$ 26.37
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BuchWeltWeit Ludwig Meier e.K.

Germany