Automatic recognition of micronucleus by combining attention mechanism and AlexNet

Abstract

Background Micronucleus (MN) is an abnormal fragment in a human cell caused by disorders in the mechanism regulating chromosome segregation. It can be used as a biomarker for genotoxicity, tumor risk, and tumor malignancy. The in vitro micronucleus assay is a commonly used method to detect micronucleus. However, it is time-consuming and the visual scoring can be inconsistent. Methods To alleviate this issue, we proposed a computer-aided diagnosis method combining convolutional neural networks and visual attention for micronucleus recognition. The backbone of our model is AlexNet without any dense layers and it is pretrained on the ImageNet dataset. Two attention modules are applied to extract cell image features and generate attention maps highlighting the region of interest to improve the interpretability of the network. Given the problems in the data set, we leverage data augmentation and focal loss to alleviate the impact. Results Experiments show that the proposed network yields better performance with fewer parameters. The AP value, F1 value and AUC value reach 0.932, 0.811 and 0.995, respectively. Conclusion In conclusion, the proposed network can effectively recognize micronucleus, and it can play an auxiliary role in clinical diagnosis by doctors.

Prediction of paroxysmal atrial fibrillation using a convolutional neural network and electrocardiogram signals

“…Due to this, no knowledge transfer took place and the network was trained initially using random weights. This type of CNN has been previously used in the classification of arrhythmia [21], and recognition [22], [23]. AlexNet contains eight layers of which 5 are convolutional and 3 are fully connected [24].…”