Development of Enhance-Net Deep Learning Approach for Performance Boosting on Medical Images
- Title
- Development of Enhance-Net Deep Learning Approach for Performance Boosting on Medical Images
- Creator
- Manoharan G.; Solanke D.R.; Acharjee P.B.; Nayak C.K.; Sharma M.K.; Sahu D.N.
- Description
- Only a few clinical procedures include the use of clinical methods for the early detection, observing, evaluation, and treatment evaluation of a range of medical illnesses. Knowing the analysis of medical images in computer vision necessitates being acquainted with the core concepts and uses of deep learning and artificial neural networks. The A rapidly expanding area of study is the Deep Learning Approach (DLA) in medical image processing. DLA is often used in medical imaging to determine if an ailment is present or not. By producing speedier, more accurate results in real time, deep learning algorithms may make the jobs of radiologists and orthopaedic surgeons easier. But the standard deep learning approach has reached its efficiencies. While offering an ideal solution known as boost-Net, we study numerous optimization strategies to increase the effectiveness of deep neural networks in this research. From a selection of well-known deep learning models, Champion-Net was selected as the deep learning model. The musculoskeletal radiograph-bone classification (MURA-BC) dataset is used in this investigation. Utilizing the train and test datasets, Enhance-Net's classification precision was evaluated. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
- Source
- Lecture Notes in Electrical Engineering, Vol-1273 LNEE, pp. 420-428.
- Date
- 2025-01-01
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- Dataset; Deep learning; Enhance- net; Medical image
- Coverage
- Manoharan G., School of Business, SR University, Telangana, Warangal, India; Solanke D.R., Department of Applied Electronics, SGB Amravati University, Maharashtra, Amravati, 444602, India; Acharjee P.B., CHRIST University, Pune, India; Nayak C.K., Faculty of Emerging Technologies, Sri Sri University, Odisha, Cuttack, India; Sharma M.K., Rungta College of Pharmaceutical Sciences and Research, Chhattisgarh, Bhilai, 490024, India; Sahu D.N., Department of MCA, Gangadhar Meher University, Odisha, Sambalpur, India
- Rights
- Restricted Access
- Relation
- ISSN: 18761100; ISBN: 978-981978030-3
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Manoharan G.; Solanke D.R.; Acharjee P.B.; Nayak C.K.; Sharma M.K.; Sahu D.N., “Development of Enhance-Net Deep Learning Approach for Performance Boosting on Medical Images,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/18924.