Brain Tumor/Mass Classification Framework Using Magnetic‐Resonance‐Imaging‐Based Isolated and Developed Transfer Deep‐Learning Model

Abstract

With the advancement in technology, machine learning can be applied to diagnose the mass/tumor in the brain using magnetic resonance imaging (MRI). This work proposes a novel developed transfer deep‐learning model for the early diagnosis of brain tumors into their subclasses, such as pituitary, meningioma, and glioma. First, various layers of isolated convolutional‐neural-network (CNN) models are built from scratch to check their performances for brain MRI images. Then, the 22‐layer, binary‐classification (tumor or no tumor) isolated‐CNN model is re‐utilized to re‐adjust the neurons’ weights for classifying brain MRI images into tumor subclasses using the transfer‐learning concept. As a result, the developed transfer‐learned model has a high accuracy of 95.75% for the MRI images of the same MRI machine. Furthermore, the developed transfer‐learned model has also been tested using the brain MRI images of another machine to validate its adapta-bility, general capability, and reliability for real‐time application in the future. The results showed that the proposed model has a high accuracy of 96.89% for an unseen brain MRI dataset. Thus, the proposed deep‐learning framework can help doctors and radiologists diagnose brain tumors early.