ResNet-50 based deep neural network using transfer learning for brain tumor classification
Madona B. Sahaai, G. R. Jothilakshmi, D. Ravikumar, Raghavendra Prasath, Saurav Singh
Abstract
Brain tumour is one of the most complicated diseases to treat in modern medicine. In the early stages of tumour development, the radiologist’s primary concern is often an accurate and efficient study. Deep Learning has become a great tool for doctors and scientists to act decisively and on time with tumor patients. A training model that has accomplish considerable result in image detection and classification is the Deep Residual Network (ResNet) utilizing CNNs. The advancement of deep learning will assist radiologists in tumor diagnostics without the use of harmful procedures. With better understanding of MRI images, as well as increase in training speeds and accuracy, deep learning can open new doors for the medical research community. In this model, an accuracy of 95.3% is achieved across various classes of brain tumor datasets. We study the outcomes of multi class classification of brain tumour using Transfer Learning utilising pre-trained ResNet50 model using CNN architecture in this paper.