Machine learning for medical image classification
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-wo...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Academia.edu Journals
2024-12-01
|
| Series: | Academia Medicine |
| Online Access: | https://www.academia.edu/126517092/Machine_learning_for_medical_image_classification |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823859684657856512 |
|---|---|
| author | Gazi Husain Jonathan Mayer Molly Bekbolatova Prince Vathappallil Mihir Matalia Milan Toma |
| author_facet | Gazi Husain Jonathan Mayer Molly Bekbolatova Prince Vathappallil Mihir Matalia Milan Toma |
| author_sort | Gazi Husain |
| collection | DOAJ |
| description |
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. |
| format | Article |
| id | doaj-art-5d7a42357eff43508aab77815f868ba6 |
| institution | Kabale University |
| issn | 2994-435X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Academia.edu Journals |
| record_format | Article |
| series | Academia Medicine |
| spelling | doaj-art-5d7a42357eff43508aab77815f868ba62025-02-10T22:25:15ZengAcademia.edu JournalsAcademia Medicine2994-435X2024-12-011410.20935/AcadMed7444Machine learning for medical image classificationGazi Husain0Jonathan Mayer1Molly Bekbolatova2Prince Vathappallil3Mihir Matalia4Milan Toma5Department of Osteopathic Manipulative Medicine, College of Osteopathic Medicine, New York Institute of Technology, Old Westbury, NY 11568, USA.Department of Osteopathic Manipulative Medicine, College of Osteopathic Medicine, New York Institute of Technology, Old Westbury, NY 11568, USA.Department of Osteopathic Manipulative Medicine, College of Osteopathic Medicine, New York Institute of Technology, Old Westbury, NY 11568, USA.Department of Osteopathic Manipulative Medicine, College of Osteopathic Medicine, New York Institute of Technology, Old Westbury, NY 11568, USA.Academic Technologies Group, College of Osteopathic Medicine, New York Institute of Technology, Old Westbury, NY 11568, USA.Department of Osteopathic Manipulative Medicine, College of Osteopathic Medicine, New York Institute of Technology, Old Westbury, NY 11568, USA. 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.https://www.academia.edu/126517092/Machine_learning_for_medical_image_classification |
| spellingShingle | Gazi Husain Jonathan Mayer Molly Bekbolatova Prince Vathappallil Mihir Matalia Milan Toma Machine learning for medical image classification Academia Medicine |
| title | Machine learning for medical image classification |
| title_full | Machine learning for medical image classification |
| title_fullStr | Machine learning for medical image classification |
| title_full_unstemmed | Machine learning for medical image classification |
| title_short | Machine learning for medical image classification |
| title_sort | machine learning for medical image classification |
| url | https://www.academia.edu/126517092/Machine_learning_for_medical_image_classification |
| work_keys_str_mv | AT gazihusain machinelearningformedicalimageclassification AT jonathanmayer machinelearningformedicalimageclassification AT mollybekbolatova machinelearningformedicalimageclassification AT princevathappallil machinelearningformedicalimageclassification AT mihirmatalia machinelearningformedicalimageclassification AT milantoma machinelearningformedicalimageclassification |