Avner Goren - research works

We developed multiple lightweight deep learning models ResNet-18 (both pretrained on ImageNet and trained from scratch), ResNet-34, ResNet-50, and a custom CNN to classify glioma, meningioma, pituitary tumor, and no tumor MRI scans.

A dataset of 7023 images was employed, split into 5712 for training and 1311 for validation.

Learning rate optimization facilitated stable convergence, and loss metrics indicated effective generalization with minimal overfitting.

Our findings confirm that a moderately sized, transfer-learned network (ResNet-18) can deliver high diagnostic accuracy and robust performance for four-class brain tumor classification.

The results indicated significant differences in gut microbiota between migraine patients and controls.

Conversely, Veillonella exhibited elevated abundance compared to controls.

Included 1,621 images for glioma, 1,645 for meningioma, 1,757 for pituitary tumors, and 2,000 for non-tumor magnetic resonance imaging (MRI) images.

After fine-tuning, the model that demonstrated the highest performance on validation data was selected as the best model Results The deep learning model demonstrated exceptional performance across all diagnostic categories, with the training accuracy stabilizing at nearly 100% and validation accuracy closely mirroring this trend.