Detect and segment brain tumors precisely and fast. It's accuracy is 94.74% in detection and 96% in segmentation so far.
+This model uses MobileNetV3 architecture to build a model which can operate both precisely and fast.
+For segmentation, DeepCancer uses its own configured U-Net architecture written by sairbarbaros.
+Since there is a few datasets for biological segmentation, I used an ultrasonic breast dataset for my model. I saved my model and weights, and you can find those in my repo.
+You can use transfer learning for applying my model and its weights to brain tumor datasets.
+You can find this brain tumor detection dataset from https://www.kaggle.com/datasets/navoneel/brain-mri-images-for-brain-tumor-detection?datasetId=165566
+You can find precision graphs and further recommendations in my repo.