Machine learning model that is able to detect and classify brain tumors in MRI scans
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Updated
Oct 23, 2024 - Python
Machine learning model that is able to detect and classify brain tumors in MRI scans
MRI modality(T1, T2, FLAIR) classification model with modified ResNet-50. Hanyang univ. dep. of biomedical engineering graduation project.
Hybrid Quantum–Classical Neural Network (QCNN) for automated brain tumour detection using MRI images. Combines EfficientNet-B0 feature extraction with a 4-qubit PennyLane quantum layer and includes a Gradio-based prediction interface.
Hybrid Quantum–Classical model for brain tumor classification using Quantum FiLM modulation and ResNet-18. Supports multi-class MRI tumor detection with quantum circuit integration.
Brain Tumor MRI Classification is an end‑to‑end deep learning project that trains multiple models (ResNet50, VGG16, a custom CNN, SVM, and Random Forest) to automatically detect and classify brain tumors from MRI scans into four classes: glioma, meningioma, pituitary, and no tumor.
Pseudo-3D CNN networks in PyTorch.
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