Classification of Sleep Arousal using Compact CNN

© 2020 IEEE. Sleep arousal is a common health problem that negatively affects the quality of sleep. This study investigates the use of a compact convolutional neural network (CNN) to classify apnea and non-apnea sleep arousal categories. The experiments are conducted on a randomly selected subset of the physiological signals provided by the PhysioNet 2018 challenge dataset. In particular, three electroencephalography (EEG) channels, two electromyography (EMG) channels, electrooculography (EOG), and airflow data are used to build the classification model. Physiological signals are down-sampled by a factor of 2 and then split into two-second long non-overlapping window segments. A data augmentation technique is then applied to overcome the large class imbalance ratio between two sleep arousal categories. The network is trained on 80% of the segments extracted from the data of 100 subjects. With only 594 trainable parameters, our approach achieves an area under the precision-recall curve (AUPRC) of 0.677 for the intra-subject test (20% of the data from the 100 subjects), and 0.183 on the inter-subject test on the data of another 12 unseen test subjects. This result falls within the range of the official scores of the challenge winners, indicating a promising application in using this lightweight CNN model for automated classification of sleep arousal

Breast Mass Tumor Classification using Deep Learning

© 2020 IEEE. This study presents pre-trained Convolutional Neural Network (CNN) models to classify pre-segmented mammogram mass tumors as benign or malignant. Transfer learning, particular pre-processing and data augmentation were applied to overcome the limitation of the scarcity of available training dataset. The proposed models are based on modified versions of Inception V3 and ResNet50 to tackle the classification problem mentioned above. The proposed architectures have been tested on the Digital Database for Screening Mammography (DDSM) dataset, and it achieved an accuracy of 0.796, precision of 0.754, and a recall of 0.891 on InceptionV3-like CNN model. On the other hand, an accuracy of 0.857, precision of 0.857, and a recall rate of 0.873 have been achieved with the ResNet50-like CNN network. Overall, the proposed ResNet50-like model achieved a 5% improvement in accuracy compared to the existing state-of-the-art method for this dataset