Related Experiment Video
Updated: Jun 5, 2025
04:48
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
Published on: November 30, 2022
2.7K
A review of convolutional neural network based methods for medical image classification
Chao Chen1, Nor Ashidi Mat Isa2, Xin Liu2
1School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, 14300, Nibong Tebal, Pulau Pinang, Malaysia; School of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin, 644000, China.
Computers in Biology and Medicine
|December 4, 2024
Summary
This review analyzes Convolutional Neural Networks (CNNs) for medical image classification, detailing techniques like transfer learning and preprocessing. While effective, challenges remain for clinical integration.
Area of Science:
- Artificial Intelligence
- Medical Imaging
- Computer Science
Background:
- Convolutional Neural Networks (CNNs) have shown significant promise in medical image classification.
- The field requires a systematic overview of current CNN-based methodologies and their applications.
Purpose of the Study:
- To systematically review and analyze CNN-based methods for medical image classification.
- To provide an in-depth overview of key techniques and challenges in this domain.
Main Methods:
- Systematic literature review of 149 recent papers on CNNs in medical image classification.
- Analysis of CNN development, data preprocessing, transfer learning, architectures, and explainability.
Main Results:
- Identified key CNN techniques enhancing classification accuracy and efficiency.
- Summarized major public datasets for disease classification using CNNs.
- Highlighted the gap between CNN performance and clinical application.
Conclusions:
- CNNs offer substantial potential for medical image analysis but face implementation hurdles.
- Future research should focus on addressing these challenges for seamless clinical integration.
- This review aids researchers and promotes deep learning in smart medical systems.
