This project implements a deep learning model for classifying brain tumors using MRI images. The model differentiates between four classes: glioma, meningioma, no tumor, and pituitary tumor.
This dataset is a combination of the following three datasets:
- figshare
- SARTAJ dataset
- Br35H
It contains 7,023 images of human brain MRIs classified into the following classes:
- Glioma
- Meningioma
- No Tumor
- Pituitary Tumor
The no tumor class images were sourced from the Br35H dataset. It was noted that the SARTAJ dataset has issues with the categorization of glioma images, leading to the removal of those images in favor of more accurate images from the figshare site.
This project implements a deep learning model for segmenting brain tumors in MRI images. The segmentation model uses a U-Net architecture to identify tumor regions in brain MRIs, specifically lower-grade gliomas. The dataset contains MRI images with manual FLAIR abnormality segmentation masks for training and validation.
The dataset used in this project was introduced in the following publications:
- Mateusz Buda, Ashirbani Saha, Maciej A. Mazurowski. "Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm." Computers in Biology and Medicine, 2019.
- Maciej A. Mazurowski, Kal Clark, Nicholas M. Czarnek, Parisa Shamsesfandabadi, Katherine B. Peters, Ashirbani Saha. "Radiogenomics of lower-grade glioma: algorithmically-assessed tumor shape is associated with tumor genomic subtypes and patient outcomes in a multi-institutional study with The Cancer Genome Atlas data." Journal of Neuro-Oncology, 2017.
This dataset includes:
- 110 patients with lower-grade gliomas.
- MRI images with FLAIR sequence.
- Manual FLAIR abnormality segmentation masks.
- Tumor genomic cluster data provided in a
data.csvfile.
The data is sourced from The Cancer Imaging Archive (TCIA) and corresponds to the lower-grade glioma collection from The Cancer Genome Atlas (TCGA).
For more details on the genomic data, please refer to the supplementary materials in this publication: Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas.
For the segmentation task, the U-Net architecture is used to identify tumor regions in MRI images. The network follows an encoder-decoder structure, with skip connections between the encoder and decoder layers to retain spatial information. The model is trained using the segmentation masks for tumor regions.