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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Survey of Supervised Learning for Medical Image Processing.

Abeer Aljuaid¹, Mohd Anwar¹

¹Department of Computer Science, North Carolina A&T State University, 1601 E Market St, Greensboro, NC 27411 USA.

SN Computer Science

|May 23, 2022

Summary

This summary is machine-generated.

Supervised deep learning significantly advances medical image analysis for disease diagnosis and treatment development. This survey details key algorithms and datasets, offering insights for researchers and practitioners in automated medical imaging.

Keywords:

Convolutional neural network (CNN)Deep learning FCN Fast R-CNN Faster R-CNN Mask R-CNN Medical image processing Supervised learning U-Net

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Area of Science:

Medical Imaging
Computer-Aided Diagnosis
Artificial Intelligence in Healthcare

Background:

Manual medical image analysis is time-consuming and labor-intensive.
Accurate automated methods are crucial for timely disease diagnosis and treatment.
Deep learning, particularly supervised learning, demonstrates human-comparable performance in medical image tasks.

Purpose of the Study:

To provide a comprehensive overview of supervised learning techniques for medical image analysis.
To guide researchers and practitioners in understanding key concepts and algorithms.
To highlight current trends and challenges in the field.

Main Methods:

Review of supervised learning performance metrics.
Summary of available medical imaging datasets.
Analysis of state-of-the-art supervised learning architectures (CNNs, R-CNNs, FCNs, U-Net).
Discussion of techniques to address data scarcity (data augmentation, transfer learning, dropout).

Main Results:

Supervised deep learning excels in medical image classification, detection, and segmentation.
Various deep learning architectures are effective for medical image processing.
Techniques like data augmentation are vital for overcoming limited labeled medical data.

Conclusions:

Supervised learning is a powerful tool for automating medical image interpretation.
Continued research in deep learning architectures and data strategies is essential for advancing medical imaging.
This survey serves as a valuable resource for the medical imaging community.