• Dr.Kiran kumar Pulamolu
• Naga Sai Bhavana Sri Challamalla
• Likitha Naga Devi Kaki
• Tarun Konathala
• Eswar Venkata Ravi Veeri

Accurate brain tumor type classification is very important for timely diagnosis and treatment planning. Therefore, it improves the outcomes of the patients. MRI is a noninvasive imaging technique that has been widely employed to generate highcontrast grayscale images of brains for diagnosing tumors. Recently, much progress has been achieved with the development of deep learning, especially CNNs, in improving the diagnostic accuracy in medical imaging. In this work, a transfer learning-based approach is adopted where several deep learning models are being fine-tuned to classify brain tumors on a three-class classification problem, namely glioma, meningioma, and pituitary tumors.

Glioma,Meningioma,Pitutary,MRI,CNN,Deeplearning

"Survey On Brain Tumor Prognosis Using Deep Learning ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 12, page no.c475-c484, December-2024, Available :http://www.jetir.org/papers/JETIR2412255.pdf

"Survey On Brain Tumor Prognosis Using Deep Learning ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 12, page no. ppc475-c484, December-2024, Available at : http://www.jetir.org/papers/JETIR2412255.pdf