Revolutionizing disease diagnosis: The role of deep learning in medical imaging and radiology

Fnu Zartashea 1, 2, *

1 Independent Researcher, USA.
2 Lead Software Engineer, USA.
 
Research Article
International Journal of Science and Research Archive, 2023, 10(01), 1175-1185.
Article DOI: 10.30574/ijsra.2023.10.1.0707
Publication history: 
Received on 21 July 2023; revised on 04 October 2023; accepted on 07 October 2023
 
Abstract: 
Medical imaging and radiology have been revolutionized by deep learning, enabling superior disease diagnosis while improving diagnostic speed and precision. The research investigates deep learning methods for medical imaging systems while focusing on their ability to identify cancer with neurological disorders and cardiovascular conditions. The research examines CNNs as important deep learning models and investigates their implementation through multiple imaging modalities while showing their ability to exceed traditional diagnostic capabilities. Our research demonstrates the operational success of deep learning techniques through real-world scanning workflows, so they improve radiological processes while minimizing errors in diagnoses. The study indicates that diagnostic AI systems enhance disease detection speed and expand radiologists' working ability through automated workflow management. Excellent breakthroughs exist in modern healthcare, but data protection issues, analytical explanation difficulties, and moral boundaries persist as substantial obstacles. Research must continue to improve deep learning applications in radiology to provide better safety levels and improve patient care effectiveness.
 
Keywords: 
AI Diagnostics; Medical Imaging; Deep Learning; Disease Detection; Radiology Workflow; Model Performance
 
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Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0