TRANSFER LEARNING IN NEURAL NETWORKS: OVERCOMING DATA SCARCITY IN MEDICAL DIAGNOSIS

Authors

  • NIÑA MARIE RACAZA Department of Education Author

Keywords:

transfer learning, neural networks, data scarcity, medical diagnosis, medical image classification, fine-tuning, domain adaptation, deep learning, resource-constrained environments

Abstract

Transfer learning has emerged as a powerful technique in overcoming the challenges of data scarcity in medical diagnosis, where obtaining large, labeled datasets can be difficult and time-consuming. This paper explores the application of transfer learning in neural networks for medical image classification and diagnosis. By leveraging pre-trained models on large, general-purpose datasets and fine-tuning them on smaller, specialized medical datasets, transfer learning allows for improved performance despite limited labeled data. The study examines various transfer learning strategies, including domain adaptation, fine-tuning, and multi-task learning, and evaluates their effectiveness in areas such as radiology, dermatology, and ophthalmology. Additionally, the paper addresses the challenges associated with transfer learning, including domain mismatch and overfitting, and discusses techniques to mitigate these issues. Finally, the potential for transfer learning to enable faster and more accurate medical diagnoses, especially in resource-constrained environments, is highlighted.

 

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Published

2024-12-28

How to Cite

TRANSFER LEARNING IN NEURAL NETWORKS: OVERCOMING DATA SCARCITY IN MEDICAL DIAGNOSIS. (2024). Cognify : Journal of Artificial Intelligence and Cognitive Science, 1(3), 40-49. https://researchvision.us/index.php/cognify/article/view/143

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