A Transfer Learning-Based Approach to Detect Cerebral Microbleeds

Cerebral microbleeds are small chronic vascular diseases that occur because of irregularities in the cerebrum vessels. Individuals and elderly people with brain injury and dementia can have small microbleeds in their brains. A recent study has shown that cerebral microbleeds could be remarkably risky in terms of life and can be riskier for patients with dementia. In this study, we proposed an efficient approach to automatically identify microbleeds by reducing the false positives in openly available susceptibility-weighted imaging (SWI) data samples. The proposed structure comprises two different pre-trained convolutional models with four stages. These stages include (i) skull removal and augmentation, (ii) making clusters of data samples using the k-mean classifier, (iii) reduction of false positives for efficient performance, and (iv) transfer-learning classification. The proposed technique was assessed using the SWI dataset available for 20 subjects. For our findings, we attained an accuracy of 97.26% with a 1.8% false-positive rate using data augmentation on the AlexNet transfer learning model and a 1.1% false-positive rate with 97.89% accuracy for the ResNet 50 model with data augmentation approaches. The results show that our models outperformed the existing approach for the detection of microbleeds.