• S. Anuradha
• R V V Sarath
• M Thanush
• D Kavya
• K Sai Pranav

During the last several years, we've come across a number of diseases that have had a significant impact on thousands of people. For the new diseases, there were few facilitations and prediction tools, and one of the primary causing issues is a brain tumor. According to current studies, histological examination is utilised to diagnose brain tumors, as opposed to more recent hospital approaches such as magnetic resonance imaging (MRI). This method is not accurate by any machine; it simply specifies brain imaging, and the clinician must determine whether or not a person has a brain tumor. We compared the prediction of brain tumors with EfficientNet b-0 and VGG 16.0 in the Convolutional Neural Network Algorithm utilizing deep learning in this project. Glioma, pituitary, meningioma, and no tumor classifications are among the tumors classified by this algorithm. EffiecientNet stores millions of images and retrieves them in an algorithm which was very useful for this project. This CNN algorithm, as well as deep learning, are critical in the development of brain tumor prediction tools in our daily lives.

Brain tumor detection, Convolutional Neural Network, Magnetic Resonance imaging, EfficientNet b-0, VGG 16.

"Multi Classification of Brain Tumor Using Deep Learning(Convolutional Neural Network Architectures)", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 5, page no.a313-a321, May-2022, Available :http://www.jetir.org/papers/JETIR2205039.pdf

"Multi Classification of Brain Tumor Using Deep Learning(Convolutional Neural Network Architectures)", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 5, page no. ppa313-a321, May-2022, Available at : http://www.jetir.org/papers/JETIR2205039.pdf