COM-MRI / config.json
NiksheyYadav
Add MRI brain classification models (93.95% tumor accuracy)
30ad36b
{
"tumor_model": {
"name": "kaggle_tumor_2dcnn_best",
"task": "image-classification",
"architecture": "2D-CNN",
"num_classes": 4,
"classes": ["glioma", "meningioma", "notumor", "pituitary"],
"input_size": [224, 224],
"channels": 3,
"parameters": 4853956,
"metrics": {
"accuracy": 0.9395,
"precision": 0.94,
"recall": 0.94,
"f1_score": 0.94
},
"per_class_accuracy": {
"glioma": 0.981,
"meningioma": 0.839,
"notumor": 0.985,
"pituitary": 0.943
},
"training": {
"dataset": "Kaggle Brain Tumor MRI",
"samples": 7023,
"epochs": 50,
"batch_size": 32,
"optimizer": "AdamW",
"learning_rate": 0.0001,
"scheduler": "OneCycleLR",
"gpu": "NVIDIA RTX 4090"
}
},
"ixi_model": {
"name": "ixi_3dcnn_best",
"task": "3d-volume-classification",
"architecture": "3D-CNN",
"num_classes": 2,
"classes": ["healthy", "diseased"],
"input_size": [64, 64, 64],
"channels": 1,
"parameters": 1196674,
"training": {
"dataset": "IXI Brain MRI",
"samples": 681,
"epochs": 30,
"batch_size": 2,
"optimizer": "Adam",
"learning_rate": 0.0001
}
},
"framework": {
"primary": "Mojo",
"inference": "PyTorch",
"version": "2.0+"
}
}