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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Deep Learning Methods for Medical Image Analysis

Deep Learning Methods for Medical Image Analysis

Rao, N. Thirupathi
Bhattacharyya, Debnath

Hardcover

DatabasesGeneral Computers

PREORDER - Expected ship date December 8, 2026

ISBN10: 1041084641
ISBN13: 9781041084648
Publisher: CRC Press
Published: Dec 8 2026
Pages: 320
Weight: 1.40
Height: 0.75 Width: 6.14 Depth: 9.21
Language: English

This volume is a useful resource on the utilization of deep learning methodologies in medical imaging, a domain that is swiftly transforming clinical diagnosis and treatment of diseases. It presents fundamental concepts in deep learning and advances to sophisticated techniques, including Convolutional Neural Networks (CNNs) for feature extraction, Fully Convolutional Networks (FCNs) and U-Nets for accurate image segmentation, and Generative Adversarial Networks (GANs) for data augmentation and image enhancement. It examines other Long Short-Term Memory (LSTM) networks for temporal analysis for elucidating intricate spatial connections in high-resolution medical pictures. It comprises a mix of theoretical background, examples of how these strategies have been put into practice, and case studies of both the triumphs and failures of these approaches in actual medical settings. This book highlights the extensive use of deep learning in several medical fields by discussing its potential applications in areas such as early illness identification, retinal imaging, histopathological categorization, and tumor and lesion detection in MRIs and CT scans. The book delves deeper than only technical topics to tackle the specific issues that arise with medical data. These include the importance of data scarcity in instances of uncommon diseases, the ethical concerns related to data privacy and security, and the necessity of interpretability to build confidence among clinicians. To help researchers overcome typical challenges, we also investigate strategies like synthetic data generation, multimodal learning, and transfer learning.

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