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





Artificial neural networks, Image classification, Learning systems, Magnetic resonance imaging, Tumors


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 the best 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. In response to the growing concern about the inaccuracies in the diagnosis of brain tumors in recent years, this paper combined deep learning and radiometric technologies and perfectly classified brain MR images with 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 us 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.