Analysis of Machine and Deep Learning Algorithms for Pattern Recognition in Medical Data
Bharath Kumar Gowru, G. Appa Rao · 2024
Pattern recognition is a data analysis technique that utilizes various algorithms for the automatic recognition of patterns and data regularities. Recently, various problems and scenes need pattern recognition for quick resolution of difficult issues, particularly those that can’t resolved by multiple dimensional data, because involved in spectral information. In this study, different machine learning (ML) and deep learning (DL) techniques are analyzed which are implemented in pattern recognition using medical data. This study discussed significant assumptions, advantages, and drawbacks of analyzed ML and DL techniques. Different ML techniques analyzed in this study are Artificial Neural Networks (ANN), Machine Learning Regression (MLR), and so on. Various DL techniques analyzed in this study are Convolutional Neural Networks (CNN), EfficientNet and so on. Different performance measures like accuracy, precision, recall, f1-score and error rates are used in previous studies for evaluation and are analyzed in this study. The study concludes that the various pattern recognition techniques have the potential to overcome every drawback and there is the option of integrating every method for developing an ensemble technique.