Reliable image metrics-based brain tumor analysis using sensor deep learning technologies

Abstract

In this paper, an attempt has been made to employ deep learning technique to predict the phase of brain tumors. The deep learning methods help the practitioners to correlate patients’ status in hand with similar subjects and assess as well as predict the future anomalies due to brain tumor. This certainly emerges as a handy tool for remote medication, especially citing the current pandemic needs. On the other hand, the digitization of the images followed by intelligent analysis can provide a first impression in a quick which usually consumes lot manual computations and cross verifications. Deep learning has been the most successful tool to handle supervised classification while dealing with complex patterns. A fully automated deep learning method can be developed with the existing toolboxes in the simulation environment. The purpose of the study is to apply this machine learning technique to classifying images of brains with different types of tumors: meningioma, glioma, and pituitary. The simulation is carried in Matlab environment and analysis is carried out using standard metrics. The confusion matrix shows a maximum of 95% and minimum of 89% accuracy. The results are compared with the Convolutional Neural Network (CNN) and Artificial Neural Network models (ANN).

Keywords

• Brain tumor
• Classification
• Deep learning
• Resnet
• Supervised learning