MATERIALS AND METHOD: In this research, we employ an open-access EEG signal public dataset containing three distinct classes: AD, FD, and control subjects. We then constructed a newly proposed EEGConvNeXt model comprised of a 2-dimensional CNN algorithm that firstly converts the EEG signals into power spectrogram-based images. Secondly, these images were used as input for the proposed EEGConvNeXt model for automated classification of AD, FD, and a control outcome. The proposed EEGConvNeXt model is therefore a lightweight model that contributes to a new image classification CNN structure based on the transformer model with four primary stages: a stem, a main model, downsampling, and an output stem.

RESULTS: The EEGConvNeXt model achieved a classification accuracy of ∼95.70% for three-class detection (AD, FD, and control), validated using a hold-out strategy. Binary classification cases, such as AD versus FD and FD versus control, achieved accuracies exceeding 98%, demonstrating the model's robustness across scenarios.

METHODS: A total of 1447 ultrasound images, including 767 benign masses and 680 malignant masses were acquired from a tertiary hospital. A semi-supervised GAN model was developed to augment the breast ultrasound images. The synthesized images were subsequently used to classify breast masses using a convolutional neural network (CNN). The model was validated using a 5-fold cross-validation method.

RESULTS: The proposed GAN architecture generated high-quality breast ultrasound images, verified by two experienced radiologists. The improved performance of semi-supervised learning increased the quality of the synthetic data produced in comparison to the baseline method. We achieved more accurate breast mass classification results (accuracy 90.41%, sensitivity 87.94%, specificity 85.86%) with our synthetic data augmentation compared to other state-of-the-art methods.