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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References

Abbas, Q. (2017). Glaucoma-Deep: Detection of Glaucoma Eye Disease on Retinal Fundus Images using Deep Learning. International Journal of Advanced Computer Science and Applications, 8(6). https://doi.org/10.14569/IJACSA.2017.080606

Anumandla, S. K. R. (2018). AI-enabled Decision Support Systems and Reciprocal Symmetry: Empowering Managers for Better Business Outcomes. International Journal of Reciprocal Symmetry and Theoretical Physics, 5, 33-41. https://upright.pub/index.php/ijrstp/article/view/129

Charron, O., Lallement, A., Jarnet, D., Noblet, V., Clavier, J-B. (2018). Automatic Detection and Segmentation of Brain Metastases on Multimodal MR Images with a Deep Convolutional Neural Network. Computers in Biology and Medicine, 95, 43-54. https://doi.org/10.1016/j.compbiomed.2018.02.004

Holmström, O., Linder, N., Ngasala, B., Mårtensson, A., Linder, E. (2017). Point-of-care Mobile Digital Microscopy and Deep Learning for the Detection of Soil-transmitted Helminths and Schistosoma Haematobium. Global Health Action, suppl. sup3, 10, 49-57. https://doi.org/10.1080/16549716.2017.1337325

Koehler, S., Dhameliya, N., Patel, B., & Anumandla, S. K. R. (2018). AI-Enhanced Cryptocurrency Trading Algorithm for Optimal Investment Strategies. Asian Accounting and Auditing Advancement, 9(1), 101–114. https://4ajournal.com/article/view/91

Madani, A., Ong, J. R., Anshul, T., Mofrad, M. R K. (2018). Deep Echocardiography: Data-efficient Supervised and Semi-supervised Deep Learning Towards Automated Diagnosis of Cardiac Disease. NPJ Digital Medicine, 1(1). https://doi.org/10.1038/s41746-018-0065-x

Maddula, S. S. (2018). The Impact of AI and Reciprocal Symmetry on Organizational Culture and Leadership in the Digital Economy. Engineering International, 6(2), 201–210. https://doi.org/10.18034/ei.v6i2.703

Maddula, S. S., Shajahan, M. A., & Sandu, A. K. (2019). From Data to Insights: Leveraging AI and Reciprocal Symmetry for Business Intelligence. Asian Journal of Applied Science and Engineering, 8(1), 73–84. https://doi.org/10.18034/ajase.v8i1.86

Mohammed, M. A., Kothapalli, K. R. V., Mohammed, R., Pasam, P., Sachani, D. K., & Richardson, N. (2017). Machine Learning-Based Real-Time Fraud Detection in Financial Transactions. Asian Accounting and Auditing Advancement, 8(1), 67–76. https://4ajournal.com/article/view/93

Møllersen, K., Zortea, M., Schopf, T. R., Kirchesch, H., Godtliebsen, F. (2017). Comparison of Computer Systems and Ranking Criteria for Automatic Melanoma Detection in Dermoscopic Images. PLoS One, 12(12), e0190112. https://doi.org/10.1371/journal.pone.0190112

Mullangi, K. (2017). Enhancing Financial Performance through AI-driven Predictive Analytics and Reciprocal Symmetry. Asian Accounting and Auditing Advancement, 8(1), 57–66. https://4ajournal.com/article/view/89

Mullangi, K., Maddula, S. S., Shajahan, M. A., & Sandu, A. K. (2018). Artificial Intelligence, Reciprocal Symmetry, and Customer Relationship Management: A Paradigm Shift in Business. Asian Business Review, 8(3), 183–190. https://doi.org/10.18034/abr.v8i3.704

Mullangi, K., Yarlagadda, V. K., Dhameliya, N., & Rodriguez, M. (2018). Integrating AI and Reciprocal Symmetry in Financial Management: A Pathway to Enhanced Decision-Making. International Journal of Reciprocal Symmetry and Theoretical Physics, 5, 42-52. https://upright.pub/index.php/ijrstp/article/view/134

Myller, K. A. H., Honkanen, J. T. J., Jurvelin, J. S., Saarakkala, S., Töyräs, J. (2018). Method for Segmentation of Knee Articular Cartilages Based on Contrast-Enhanced CT Images. Annals of Biomedical Engineering, 46(11), 1756-1767. https://doi.org/10.1007/s10439-018-2081-z

Nizamuddin, M., Natakam, V. M., Sachani, D. K., Vennapusa, S. C. R., Addimulam, S., & Mullangi, K. (2019). The Paradox of Retail Automation: How Self-Checkout Convenience Contrasts with Loyalty to Human Cashiers. Asian Journal of Humanity, Art and Literature, 6(2), 219-232. https://doi.org/10.18034/ajhal.v6i2.751

Olsen, T., Jackson, B., Feeser, T., Kent, M., Moad, J. (2018). Diagnostic Performance of Deep Learning Algorithms Applied to Three Common Diagnoses in Dermatopathology. Journal of Pathology Informatics, 9(1), 32-32. https://doi.org/10.4103/jpi.jpi_31_18

Patel, B., Mullangi, K., Roberts, C., Dhameliya, N., & Maddula, S. S. (2019). Blockchain-Based Auditing Platform for Transparent Financial Transactions. Asian Accounting and Auditing Advancement, 10(1), 65–80. https://4ajournal.com/article/view/92

Pydipalli, R. (2018). Network-Based Approaches in Bioinformatics and Cheminformatics: Leveraging IT for Insights. ABC Journal of Advanced Research, 7(2), 139-150. https://doi.org/10.18034/abcjar.v7i2.743

Richardson, N., Pydipalli, R., Maddula, S. S., Anumandla, S. K. R., & Vamsi Krishna Yarlagadda. (2019). Role-Based Access Control in SAS Programming: Enhancing Security and Authorization. International Journal of Reciprocal Symmetry and Theoretical Physics, 6, 31-42. https://upright.pub/index.php/ijrstp/article/view/133

Rodriguez, M., Tejani, J. G., Pydipalli, R., & Patel, B. (2018). Bioinformatics Algorithms for Molecular Docking: IT and Chemistry Synergy. Asia Pacific Journal of Energy and Environment, 5(2), 113-122. https://doi.org/10.18034/apjee.v5i2.742

Sachani, D. K. (2018). Technological Advancements in Retail Kiosks: Enhancing Operational Efficiency and Consumer Engagement. American Journal of Trade and Policy, 5(3), 161–168. https://doi.org/10.18034/ajtp.v5i3.714

Sachani, D. K., & Vennapusa, S. C. R. (2017). Destination Marketing Strategies: Promoting Southeast Asia as a Premier Tourism Hub. ABC Journal of Advanced Research, 6(2), 127-138. https://doi.org/10.18034/abcjar.v6i2.746

Shajahan, M. A. (2018). Fault Tolerance and Reliability in AUTOSAR Stack Development: Redundancy and Error Handling Strategies. Technology & Management Review, 3, 27-45. https://upright.pub/index.php/tmr/article/view/126

Shajahan, M. A., Richardson, N., Dhameliya, N., Patel, B., Anumandla, S. K. R., & Yarlagadda, V. K. (2019). AUTOSAR Classic vs. AUTOSAR Adaptive: A Comparative Analysis in Stack Development. Engineering International, 7(2), 161–178. https://doi.org/10.18034/ei.v7i2.711

Vennapusa, S. C. R., Fadziso, T., Sachani, D. K., Yarlagadda, V. K., & Anumandla, S. K. R. (2018). Cryptocurrency-Based Loyalty Programs for Enhanced Customer Engagement. Technology & Management Review, 3, 46-62. https://upright.pub/index.php/tmr/article/view/137

Yarlagadda, V. K., & Pydipalli, R. (2018). Secure Programming with SAS: Mitigating Risks and Protecting Data Integrity. Engineering International, 6(2), 211–222. https://doi.org/10.18034/ei.v6i2.709

Ying, D., Patel, B., & Dhameliya, N. (2017). Managing Digital Transformation: The Role of Artificial Intelligence and Reciprocal Symmetry in Business. ABC Research Alert, 5(3), 67–77. https://doi.org/10.18034/ra.v5i3.659

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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.my/index.php/mjmbr/article/view/687