Optimization and efficiency analysis of deep learning based brain tumor detection

Abstract

Brain tumors are spreading very fast across the world. It is one of the aggressive diseases which eventually lead to death if not being detected timely and appropriately. The difficult task for neurologists and radiologists is detecting brain tumor at early stages. However, manually detecting brain tumor from magnetic resonance imaging images is challenging, and susceptible to errors as experienced physician is required for this. To resolve both the concerns, an automated brain tumor detection system is developed for early diagnosis of the disease. In this paper, the diagnosis via MRI images are being done along with classification in terms of its type. The proposed system can specifically classify four brain tumor condition classification like meningioma tumor, pituitary tumor, glioma tumor and no tumor. The convolutional neural network method is used for classification and diagnosis of tumors which has accuracy of about 93.60%. This study is done on a KAGGLE dataset which comprises of 3274 Brain MRI scans. This model can be applied for real time brain tumor detection.