A Deep Transfer Learning Based Architecture for Brain Tumor Classification Using MR Images

Deep Learning (DL) is becoming more popular in the healthcare sectors due to the exponential growth of data availability and its excellent performance in diagnosing various diseases. This paper has aimed to design a new possible brain tumor diagnostic model to improve accuracy and reliability of radiology. In this paper, an advanced deep learning algorithm is used to detect and classify brain tumors in magnetic resonance (MR) images. Diagnosing brain tumors in radiology is a significant issue, yet it is a difficult and time-consuming procedure that radiologists must pass through. The reliability of their assessment relies completely on their knowledge and personal judgements which are in most cases inaccurate. As a possible remedy to the growing concern in diagnosing brain tumors accurately, in this work a deep learning method is applied to classify the brain tumor MR images with very high performance accuracy. The research leveraged a transfer learning model known as AlexNet's convolutional neural network (CNN) to perform this operation. Our method helps to improve robustness, efficiencies and accuracy in the healthcare sector with the ability to automate the entire diagnostic process with the overall accuracy of 99.62%. Additionally, our model has the ability to detect and classify tumors at their different stages and magnitudes.

Automated summary

This paper introduces a new brain tumor diagnostic model using deep learning algorithm, aiming to improve the accuracy and reliability of radiology in detecting and classifying brain tumors in MR images, achieving an overall accuracy of 99.62% and enhancing efficiency in the healthcare sector.