A hybrid CNN-FC approach for automatic grading of brain tumors from non-invasive MRIs

The grading of brain tumors is essential in treatment planning to effectively control the tumor growth and reduce the associated symptoms. Appropriate treatment planning might help in improving the quality of life and patient life span. Gliomas are indeed the most common type of brain tumor, originating from glial cells. Low-grade gliomas (grades 1 or 2) are typically slow-growing, less invasive, and may be suitable for surgical resection or targeted therapies. On the other hand, higher-grade tumors such as grades 3 or 4 are more aggressive, it might infiltrate the surrounding brain tissue making complete resection challenging. In clinical diagnosis, traditionally tumor grading requires the procedure of resecting a part of the tumor for microscopic examination. To address this, a method to grade the tumor non-invasively using MRIs is proposed. Our work utilized the BraTS2018 dataset to segment the substructure of brain tumors that includes necrosis and non-enhancing, edema, and enhancing regions. These regions are then used to train the proposed grading model. Furthermore, we evaluated the performance of our model on a tertiary hospital dataset consisting of 69 samples. The accuracy scores obtained on the BraTS2018 test sample and tertiary hospital dataset are 0.87 and, 0.85 respectively. This consistent score on both public and tertiary hospital datasets indicates a reliable and stable performance of the model.