Advancements in Medical Image Analysis: A Metric-driven review of AI, ML, and DL Methods
DOI:
https://doi.org/10.26438/ijcse/v13i10.1423Keywords:
Medical Image Analysis (MIA), Machine Learning (ML), Artificial Intelligence (AI), Deep Learning (DL), Convolutional Neural Networks (CNN)Abstract
The domain of medical image analysis (MIA) within Machine Learning (ML), Artificial Intelligence (AI), and Deep Learning (DL), the significance of employing advanced methods cannot be overstated. Several methods have attained better outcomes in several fields, making it especially noteworthy for MIA in healthcare. The combination of these fields with MIA gives real-time analysis of prior and different databases, comprehensive insights that are important to improve healthcare results, and operational efficacy in the industry. This analysis article of existing literature considers a thorough examination of the most current ML, DL, and AI methods designed to identify the complexities faced in medical healthcare, specifically focusing on the utilization of DL, ML, and AI methods in MIA. The main contribution of this paper is how AI, ML and DL is used in medical field for early disease detection, drug discovery, and robotic-assisted surgeries. Comparative Analysis Based on the Different models and Algorithm is properly defined in this paper. It analysed the different methods, such as convolutional neural networks(CNNs), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), etc. This paper review the existing result was analysed, by other authors with a high accuracy achieved with the CNN method of 94%, XGBoost achieved the maximum accuracy of 91%, and Logistic Regression an accuracy of 79% and compared with the existing models.
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