Bone Abnormality Detection Using RMSprop Optimizer in VGG16

Title

Bone Abnormality Detection Using RMSprop Optimizer in VGG16

Creator

Chaitra, P.C.; Avnish, Aryan

Description

The advent of deep learning has revolutionized medical imaging, enhancing diagnostic precision, treatment planning, and patient care. This study leverages deep learning, specifically employing the VGG16 model optimized with RMSprop, to automate bone abnormality detection. Methodologically, the research encompasses data acquisition, preprocessing, and model training with RMSprop optimization. Results highlight the efficacy of this approach, showcasing RMSprops ability to detect various bone abnormalities. These findings underscore deep learnings potential in medical imaging, emphasizing its applicability beyond bone abnormality detection. The study illuminates the transformative impact of RMSprop-optimized deep learning models in medical imaging, promising advancements in automated diagnosis and treatment planning. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Source

Smart Innovation, Systems and Technologies;Volume;413 SIST;pp.305-316

Date

01-01-2025

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Bone abnormality detection; RMSprop optimizer; VGG16

Coverage

Chaitra P.C., Computer Science and Engineering, Christ University, Bangalore, India; Avnish A., Computer Science and Engineering, Christ University, Bangalore, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 21903018; ISBN: 978-981977716-7;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-97-7717-4_21

https://www.scopus.com/pages/publications/86000450181?origin=resultslist