Comparative analysis of web-based machine learning models

Abstract

This paper presents a comparative analysis of web-based machine learning models, specifically examining Google Vertex AI, Google Teachable Machine, Azure Machine Learning and Salesforce Einstein Vision. The objective is to assess their suitability for integration into a medical information system as a classification module for medical images. The comparative evaluation considers factors such as model accuracy, ease of integration and scalability. The findings aim to guide the selection of an optimal machine learning solution for enhancing the capabilities of medical image classification within a healthcare context.

Keywords

• healthcare
• web-based machine learning models
• decision support
• classification module
• medical image analysis