Convolutional Neural Networks for Enhancing Clinical Decision-Making

Kiran Sree Pokkuluri and SSSN Usha Devi N
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Kiran Sree Pokkuluri: Professor & Head, Department of Computer Science and Engineering, Shri Vishnu Engineering College for Women, India
SSSN Usha Devi N: Department of Computer Science and Engineering, University College of Engineering, India

Biomedical Journal of Scientific & Technical Research, 2024, vol. 56, issue 3, 48124-48127

Abstract: Convolutional Neural Networks (CNNs) are a powerful technology that may be used to analyses medical imaging data and improve clinical decision-making. This abstract emphasizes their significance and ramifications for the medical field. CNNs are highly proficient in image segmentation, classification, and detection, which makes it possible to interpret medical images like MRIs, CT scans, and X-rays accurately and quickly. CNNs help physicians diagnose conditions, evaluate the effectiveness of treatments, and plan interventions more quickly and precisely by automatically recognizing and emphasizing important elements in images. Furthermore, CNNs enable the creation of computer-aided diagnostic systems that supplement human expertise by facilitating the integration of cutting-edge imaging technologies into clinical workflows. These technologies provide insightful decision assistance, assisting medical professionals in identifying small irregularities and formulating wise treatment suggestions.

Keywords: Journals on Medical Drug and Therapeutics; Journals on Emergency Medicine; Physical Medicine and Rehabilitation; Journals on Infectious Diseases Addiction Science and Clinical Pathology; Open Access Clinical and Medical Journal; Journals on Biomedical Science; List of Open Access Medical Journal; Journals on Biomedical Engineering; Open Access Medical Journal; Biomedical Science Articles; Journal of Scientific and Technical Research (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:abf:journl:v:56:y:2024:i:3:p:48124-48127

DOI: 10.26717/BJSTR.2024.56.008859

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