With the continuous development of medical imaging technology, computed tomography (CT) plays an increasingly important role in clinical diagnosis. The quality of CT images directly affects the diagnostic accuracy and treatment effectiveness of diseases. In recent years, deep learning technology has shown great potential as an emerging image analysis method in CT image quality detection. This article reviews the traditional methods of medical CT image quality detection, analyzes their limitations, and proposes an improvement strategy based on deep learning. By optimizing and innovating existing detection methods, combined with the automatic feature extraction capability of deep learning, this paper aims to improve the accuracy, real-time performance, and consistency of CT image quality detection. In the future, with the continuous advancement of technology, image quality detection methods based on deep learning will be more widely applied in the field of medical imaging, promoting the development of medical image analysis towards greater intelligence and precision.

Yu Wang. Traditional Detection Methods and Improvement Strategies for Medical CT Image Quality. Frontiers in Medical Science Research (2025), Vol. 7, Issue 1: 40-46. https://doi.org/10.25236/FMSR.2025.070106.

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