Machine learning for medical image classification
Year: 2024
Type: article
Abstract: This review article focuses on the application of machine learning (ML) algorithms in medical image classification. It highlights the intricate process involved in selecting the most suitable ML algorithm for predicting specific medical conditions, emphasizing the critical role of real-world data in testing and validation. It navigates through various ML methods utilized in healthcare, including Supervised Learning, Unsupervised Learning, Self-Supervised Learning, Deep Neural Networks, Reinforcement Learning, and Ensemble Methods. The challenge lies not just in the selection of an ML algorithm but in identifying the most appropriate one for a specific task as well, given the vast array of options available. Each unique dataset requires a comparative analysis to determine the best-performing algorithm. However, testing all available algorithms is impractical. This article examines the performance of various ML algorithms in recent studies, focusing on their applications across different imaging modalities for diagnosing specific medical conditions. It provides a summary of these studies, offering a starting point for those seeking to select the most suitable ML algorithm for specific medical conditions and imaging modalities.
Source: Academia Medicine
DOI: 10.20935/acadmed7444
Authors: Gazi Husain, Jonathan Mayer, Molly Bekbolatova, Prince Vathappallil, Mihir Matalia, Milan Toma
Institution: New York Institute of Technology
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4405698177