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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

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Published on: January 7, 2019

Medical image identification methods: A review.

Juan Li¹, Pan Jiang², Qing An³

¹School of Information Engineering, Wuhan Business University, Wuhan, 430056, China; School of Artificial Intelligence, Wuchang University of Technology, Wuhan, 430223, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China.

Computers in Biology and Medicine

|December 17, 2023

Summary

This summary is machine-generated.

This paper reviews advanced artificial intelligence methods like deep learning for medical image identification. It highlights their application in diagnosis, segmentation, and detection across various medical fields.

Keywords:

Classification Deep learning Medical image identification Transfer learning

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Area of Science:

Medical Imaging Analysis
Artificial Intelligence in Healthcare
Computer-Aided Diagnosis

Background:

Medical image identification is crucial for computer-aided diagnosis, retrieval, and mining.
Intelligent imaging offers advantages over traditional methods but faces challenges due to diverse modalities and pathologies.
Existing methods analyze electronic health records and gene information.

Purpose of the Study:

To comprehensively review and summarize recent studies on artificial intelligence methods for medical image analysis.
To analyze and discuss the application of machine learning, deep learning, and convolutional neural networks in medical imaging.
To provide an overview of current progress, challenges, and future research directions in the field.

Main Methods:

Review of recent studies applying machine learning, deep learning, convolutional neural networks, and transfer learning.
Analysis of image processing technologies for medical image identification.
Categorization of methods based on application scenarios (classification, segmentation, detection, registration) and areas (pulmonary, brain, digital pathology, etc.).

Main Results:

Deep learning and related AI methods show significant promise in various medical image analysis tasks.
Methods are summarized based on application scenarios and specific medical areas, demonstrating broad applicability.
The review highlights the latest progress and contributions of different AI techniques.

Conclusions:

AI, particularly deep learning, is transforming medical image analysis, offering powerful tools for diagnosis and research.
Future research should focus on addressing open challenges and exploring novel applications, potentially integrating computer vision and natural language processing.
Continued advancements in AI algorithms will further enhance the capabilities of medical image recognition and interpretation.