The brain tumor, are the most common and deadly disease, leading to a very short life span in their highest grade. Thus, planning treatment is a key stage to improve the life span of the patients. Generally, various image techniques such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and ultrasound image are used to valuate the tumor in a brain, lung, liver, breast, prostate…etc. Especially, in this the MRI images are used to diagnose tumor in the brain. A huge amount of data is generated by MRI scan through manual classification of tumor vs non-tumor in a particular time. But they are having some limitation therefore, an accurate measurement is provided for limited number of images. They are trusted and automatic classification scheme are essential to prevent the death rate of the affected person. The brain tumor classification is very challenging task in the large structural variability of surrounding region of the brain tumor. In this brain tumor detection is proposed by using Convolutional Neural Networks (CNN) classification. The deepest architecture design is performed by using small kernels. The weight of the neuron is given tiny.
