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MRI modality(T1, T2, FLAIR) classification model with modified ResNet-50. Hanyang univ. dep. of biomedical engineering graduation project.

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Name		Name	Last commit message	Last commit date
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losses		losses
models		models
preprocessing_visualizations		preprocessing_visualizations
scripts		scripts
.gitignore		.gitignore
README.md		README.md

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Brain MRI Modality(T1, T2, FLAIR) Classification with Modified ResNet-50

Usage

Preprocess your MRI images(3D NIfTI) with python scripts/preprocess_images.py
Train your model with python scripts/train.py
Test your model with python scripts/test.py default oasis3

Datasets

Dataset	Modality	Details
ADNI1	T1	MPRAGE
ADNI1	T2	T2-FSE
ADNI2	T1	MPRAGE
ADNI2	FLAIR	FLAIR (Axial)
ADNI3	T1	MPRAGE (Sagittal)
ADNI3	FLAIR	FLAIR (Sagittal)
ADNIGO	T1	MPRAGE
ADNIGO	FLAIR	FLAIR (Axial)
CAMCAN	T1
CAMCAN	T2
IXI	T1
IXI	T2
Kirby-21	T1	MPRAGE
Kirby-21	T2
Kirby-21	FLAIR
MICCAI2017	T1
MICCAI2017	FLAIR
MICCAI2018	T1
MICCAI2018	T2
MICCAI2018	FLAIR

References

Çinar, A., & Yildirim, M. (2020). Detection of tumors on brain MRI images using the hybrid convolutional neural network architecture. Medical hypotheses, 139, 109684.

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MRI modality(T1, T2, FLAIR) classification model with modified ResNet-50. Hanyang univ. dep. of biomedical engineering graduation project.

machine-learning deep-neural-networks deep-learning mri resnet resnet-50 mri-classification

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