10.46243/jst.2021.v6.i04.pp131-136 registered

The brain tumors, are the most common and aggressive disease and it is challenging task to detect brain tumor in early stages of life, it leads to a very short life expectancy in their highest grade. Thus, treatment planning will be a key stage to improve the quality of life of patients. To evaluate the tumor in a brain used various image techniques such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and ultrasound image etc. Mostly, in this work MRI images are used to diagnose tumor in the brain. The huge amount of data generated by MRI scan that helps to classify tumor vs non-tumor in a particular time. But it having some limitation (i.e.) accurate quantitative measurements will be provided for limited number of images. To prevent death rate of human trusted and automatic classification scheme are essential. The automatic brain tumor classification will be very challenging task in large spatial and structural variability of surrounding region of brain tumor. In this work, automatic brain tumor detection will be proposed by using Convolutional Neural Networks (CNN) classification. The deeper architecture design will be performed by using small kernels. The weight of the neuron will be given as small.