Diagnosis of Brain Tumor using Automated Deep Learning Model

Brain tumor can be signified as a contorted mass of tissue centred in the cells increase unexpectedly and interminably, that is there is no power over the development of the cells. The early discovery of brain tumor is necessary to improve the quality of life of an individual. For precise and mechanized classification of mind MRI, there are numerous methodologies in existence related to the binary class classification of brain MRI scans. The problem of classification where we have several categories is a more complex task. Here, a strategy is presented that is equipped for classifying the brain MRI scans into four classes namely, glioma tumor, pituitary tumor, meningioma tumor and normal brain. The proposed model is essentially isolated into three most significant stages: - 1. Pre-processing of MRI scans. 2. Feature extraction with the assistance of pre-trained VGG16 model. 3. Classification of the MRI scans using Random Forest. The assessment of the model is finished by calculating its accuracy and it has obtained 95.4% accuracy. Moreover, we have contrasted our suggested model with the other pre-trained models like VGG16, VGG19, and AlexNet. Examinations with these models demonstrate the prevalence of the proposed strategy.