Deep learning approaches for classification tasks in medical X-ray, MRI, and ultrasound images: a scoping review

Abstract

Medical images occupy the largest part of the existing medical information and dealing with them is challenging not only in terms of management but also in terms of interpretation and analysis. Hence, analyzing, understanding, and classifying them, becomes a very expensive and time-consuming task, especially if performed manually. Deep learning is considered a good solution for image classification, segmentation, and transfer learning tasks since it offers a large number of algorithms to solve such complex problems. PRISMA-ScR guidelines have been followed to conduct the scoping review with the aim of exploring how deep learning is being used to classify a broad spectrum of diseases diagnosed using an X-ray, MRI, or Ultrasound image modality.Findings contribute to the existing research by outlining the characteristics of the adopted datasets and the preprocessing or augmentation techniques applied to them. The authors summarized all relevant studies based on the deep learning models used and the accuracy achieved for classification. Whenever possible, they included details about the hardware and software configurations, as well as the architectural components of the models employed. Moreover, the models that achieved the highest accuracy in disease classification were highlighted, along with their strengths. The authors also discussed the limitations of the current approaches and proposed future directions for medical image classification.

Keywords: Deep learning; MRI; Medical image classification; Ultrasound; X-ray.

Fig. 1

Flow of the study selection process

Fig. 2

Search queries performed in the chosen databases

Fig. 3

Papers published annually from 2016 to 2024

Fig. 4

Frequency of studies using a specific medical imaging modality for different dataset sizes and accessibility

Fig. 5

Adoption of preprocessing & augmentation by the eligible studies

Fig. 6

Percentage of papers using a specific preprocessing technique

Fig. 7

Percentage of papers using a specific augmentation approach