EfficientNet Transfer Learning Approach for Multi-Class Brain Tumor Classification

EfficientNet Transfer Learning Approach for Multi-Class Brain Tumor Classification

Mrs.P.Gokila1, Mr.M.Nagarasan2, Anandhi.R3, Manikandan.B4, Sharan.V.J5,

Sham Daniel.S6

1, 2 Assistant professor, Department of Computer Science and Engineering, Info Institute of Engineering

3,4,5,6 UG Scholars, Department of Computer Science and Engineering, Info Institute of Engineering

Abstract- It can be difficult for radiologists to accurately diagnose brain tumors in the medical field. When radiologists manually identify tumors, it can result in incorrect treatment planning and medical errors. The Miami Neuroscience Center states that there are 120 different kinds of brain tumors that can impact an individual's brain. Brain tumors that are most common and dangerous include glioma, meningioma, and pituitary tumors. In this work, we developed an online diagnostic tool to accurately classify brain tumors, including Astrocytoma, Glioma, Meningioma, Neurocytoma and Pituitary. Additionally, our program is capable of classifying a normal brain. We added CT and MRI scan images to improve cross-modality and model capability. We employed the CNN based neural network architecture known as EfficientNet. With this model, we achieved over 93.4% accuracy. Our application will help the radiologist identify tumors quickly and promote better treatment planning.

Key words: Deep Learning, Transfer Learning, CNN, MRI, CT, Multi-Class Classification.