Automatic cerebral microbleeds detection from MR images via multi-channel and multi-scale CNNs

Abstract

Background

Computer-aided detection (CAD) systems have been widely used to assist medical professionals in interpreting medical images, aiding in the detection of potential diseases. Despite their usefulness, CAD systems cannot yet fully replace doctors in diagnosing many conditions due to limitations in current algorithms. Cerebral microbleeds (CMBs) are a critical area of concern for neurological health, and accurate detection of CMBs is essential for understanding their impact on brain function. This study aims to improve CMB detection by enhancing existing machine learning algorithms.

Methods

This paper presents four CNN-based algorithms designed to enhance CMB detection. The detection methods are categorized into traditional machine learning approaches and deep learning-based methods. The traditional methods, while computationally efficient, offer lower sensitivity, while CNN-based approaches promise greater accuracy. The algorithms proposed in this study include a multi-channel CNN with optimized architecture and a multiscale CNN structure, both of which were designed to reduce false positives and improve overall performance.

Results

The first CNN algorithm, with an optimized multi-channel architecture, demonstrated a sensitivity of 99.6 %, specificity of 99.3 %, and accuracy of 99.5 %. The fourth algorithm, based on a stable multiscale CNN structure, achieved sensitivity of 98.2 %, specificity of 97.4 %, and accuracy of 97.8 %. Both algorithms exhibited a significant reduction in false positives compared to traditional methods. The experiments conducted confirm the effectiveness of these algorithms in improving the precision and reliability of CMB detection.

Conclusion

The proposed CNN-based algorithms demonstrate a significant advancement in the automated detection of CMBs, with notable improvements in sensitivity, specificity, and accuracy. These results underscore the potential of deep learning models, particularly CNNs, in enhancing CAD systems for neurological disease detection and reducing diagnostic errors. Further research and optimization may allow these algorithms to be integrated into clinical practices, providing more reliable support for healthcare professionals.