Shallow and deep learning classifiers in medical image analysis

The authors of this review aimed to give educational insight into the most accessible and widely employed classifiers in the field of radiology, distinguish between “shallow” learning algorithms, as well as look into “deep” learning architectures such as convolutional neural networks and vision transformers. This review found that machine learning classifiers offer vital information for the development of clinical decision support systems in healthcare. Key points: Training a shallow classifier requires extracting disease-related features from regions of interest (e.g., radiomics). Deep classifiers implement automatic feature extraction and classification. The classifier selection is based on data and computational resources availability, task, and explanation needs. Article: Shallow and deep learning classifiers in medical image analysis Authors: Francesco Prinzi, Tiziana Currieri, Salvatore Gaglio & Salvatore Vitabile