Deep Learning-Enhanced Image Segmentation for Medical Diagnostics

Authors

  • Srinivas Addimulam Senior Manager (Lead Data Engineer), CVS Health, 909 E Collins Blvd, Richardson, TX, 75081, USA
  • Manzoor Anwar Mohammed Oracle EBS Developer, Chicago Public Schools, 42 W Madison St, Chicago, IL – 60602, USA
  • Raghunath Kashyap Karanam Senior Associate Consultant, Cisco Systems, Inc., 300 East Tasman Dr. San Jose, CA 95134, USA
  • Deng Ying Lecturer, Jiujiang Vocational and Technical College, Jiujiang, Jiangxi, China
  • Rajani Pydipalli Sr. SAS Programmer, Cytel Inc., 1050 Winter St # 2700, Waltham, MA 02451, USA
  • Bhavik Patel PCB Design Engineer, Innovative Electronics Corp., Pittsburgh, PA 15205, USA
  • Mohamed Ali Shajahan Sr. Staff SW Engineer, Continental Automotive Systems Inc., Auburn Hills, MI 48326, USA
  • Niravkumar Dhameliya PLC Programmer, Innovative Electronics Corporation, Pittsburgh, PA, USA
  • Vineel Mouli Natakam Sr SAP Order to Cash Consultant, United Software Group Inc., Dublin, OH 43017, USA

Keywords:

Deep Learning, Image Segmentation, Medical Diagnostics, Computer-Aided Diagnosis, Convolutional Neural Networks, Healthcare Imaging, Pixel-level Classification, Radiological Interpretation

Abstract

Deep learning-enhanced picture segmentation has transformed medical diagnostics by accurately and efficiently delineating anatomical features and clinical anomalies. This article examines how deep learning affects medical image segmentation, identifies the main methods, and evaluates the results and obstacles. This study covers recent field research and innovations using secondary data. CNNs, attention mechanisms, and generative models like GANs have increased segmentation performance in neuroimaging, oncology, cardiology, pathology, and radiology. However, issues must still be solved with model interpretability, dependency on massive annotated datasets, and imaging technique variability. Policy implications emphasize the need for consistent imaging methods, data-sharing agreements, and explainable AI to build clinical trust and acceptance. Federated learning requires reformed data privacy laws to protect patient privacy and enable collaborative model development. Innovative research and deliberate policy actions can improve deep learning in medical diagnostics, increasing patient care and clinical outcomes.

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Published

2020-11-25

Issue

Vol. 7 No. 2 (2020): July - December Issue

Section

Peer-reviewed Article

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Copyright (c) 2020 Malaysian Journal of Medical and Biological Research

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How to Cite

Addimulam, S., Mohammed, M. A., Karanam, R. K., Ying, D., Pydipalli, R., Patel, B., Shajahan, M. A., Dhameliya, N., & Natakam, V. M. (2020). Deep Learning-Enhanced Image Segmentation for Medical Diagnostics. Malaysian Journal of Medical and Biological Research, 7(2), 145-152. https://mjmbr.codexcafe.net/index.php/mjmbr/article/view/687