A Comparative Study of Machine Learning Algorithms for Image Recognition in Medical Imaging

Arjun Krishnaiah Somanakoppa

Authors

Arjun Krishnaiah Somanakoppa Independent Researcher Author

Keywords:

Medical imaging, Image recognition, Machine learning algorithms, CNN, SVM

Abstract

Medical imaging plays a fundamental role in the diagnosis and treatment of various diseases. With the advent of machine learning, there has been a growing interest in developing efficient algorithms for image recognition in medical imaging. This research paper presents a comprehensive comparative study of machine learning algorithms applied to image recognition in the context of medical imaging. The study encompasses a diverse range of machine learning techniques, including but not limited to convolutional neural networks (CNNs), support vector machines (SVMs), decision trees, and random forests. A dataset comprising medical images from different modalities and pathology types is utilized to assess the algorithms' performance in terms of accuracy, sensitivity, specificity, and computational efficiency. Through rigorous experimentation and analysis, the research investigates the strengths and weaknesses of each algorithm, providing insights into their applicability for various medical imaging tasks. Special attention is given to the interpretability and generalizability of the models, considering the critical nature of medical diagnoses.

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