Deep Learning-based Transfer Learning Model in Diagnosis of Diseases with Brain Magnetic Resonance Imaging

Abstract

Brain MRI images alone is carried out over the last 5 years. The inference drawn from this work is that a hybrid architecture based on transfer learning produced more than 90% accuracy in most of the cases with minimal training time. In hybrid architecture, more than one pre-trained models are integrated to extract high-level features. Pre-trained models are good at recognising high-level features like edges, patterns, and so on. The model designed with pre-trained model starts with learned weights rather than assigning a random value. This promotes faster convergence and, as a result, reduces the amount of time required to train the model.

Topics & Concepts