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Convolutional Neural Network and Transfer Learning-based Approach for Brain Tumor Detection in Magnetic Resonance Imaging

Kirana D N, Sameeksha ., Chandana ., Shreya Devadiga, Srijanya .


Brain tumors are among the most invasive illnesses that can affect both children and adults. Brain tumors develop very quickly, and if not treated at the proper time, they decrease the patient's chances of survival. It is crucial to find brain tumors at an early stage. To increase patients’ life expectancy, proper treatment planning and precise diagnostics are most important. The best way to detect brain tumors is via Magnetic Resonance Imaging (MRI). Therefore, to find the types of tumors, an automated brain tumor detection system is needed. This work uses Deep Learning (DL) architectures like Convolutional Neural Network (CNN) and EfficientNetB0 for Transfer Learning to detect the brain tumor. This model is used to predict the types of brain tumors.


Deep Learning, Convolution Neural Network, EfficientNetB0, Brain tumors, MRI.

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