Abstract
Deep learning (DL) has seen an exponential development in recent years, with major impact in many medical fields, especially in the field of medical image. The purpose of the work converges in determining the importance of each component, describing the specificity and correlations of these elements involved in achieving the precision of interpretation of medical images using DL. The major contribution of this work is primarily to the updated characterisation of the characteristics of the constituent elements of the deep learning process, scientific data, methods of knowledge incorporation, DL models according to the objectives for which they were designed and the presentation of medical applications in accordance with these tasks. Secondly, it describes the specific correlations between the quality, type and volume of data, the deep learning patterns used in the interpretation of diagnostic medical images and their applications in medicine. Finally presents problems and directions of future research. Data quality and volume, annotations and labels, identification and automatic extraction of specific medical terms can help deep learning models perform image analysis tasks. Moreover, the development of models capable of extracting unattended features and easily incorporated into the architecture of DL networks and the development of techniques to search for a certain network architecture according to the objectives set lead to performance in the interpretation of medical images.
Highlights
The medical data most used in medical practice are medical images and for this reason most deep learning algorithms have targeted this category of medical information for the realization of medical applications.This paper presents a methodical review of the literature [1] with the objective of carrying out an analysis of the importance of the relationship between the types and characteristics of scientific data and their use of deep learning models in the interpretation of medical images
Image analysis performance is enhanced by the use of the following architectures: AlexNet, VGGNet and ResNet, YOLO or U-net that we describe below: AlexNet was proposed by [58] [59] for the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2012 [4]
Annotations and labels, identification and automatic extraction of specific medical terms can help deep learning models perform in the tasks of image analysis [3]
Summary
The medical data most used in medical practice are medical images and for this reason most deep learning algorithms have targeted this category of medical information for the realization of medical applications. The uniqueness of the work is defined by the description of all the constituent elements, namely: data, identification and extraction of automatic standardization of specific medical terms, representation of medical knowledge, incorporation of medical knowledge labeling, description of deep learning (DL) architectures in relation to the objectives for which they were created and in correlation with the other constituent elements of the DL process, presentation of the applications for which they were constituted. We aim to achieve an updated characterization of the specifics of the constituent elements of the deep learning process, scientific data, methods of incorporation of knowledge, DL models according to the objectives for which they were designed and presentation of medical applications according to these tasks. The type and volume of medical data, the labels, the category of field knowledge and the methods of their integration into the DL architectures implicitly determine their performance in medical applications
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