A Novel Approach of Transfer Learning for Accurate Brain Tumor MRI Classification in Big Data Healthcare Environment

Abstract

The complex structure of brain tumors and the need for prompt and accurate detection present a major challenge to the medical community. This paper suggests an improved CNN framework for classifying brain cancers in the big data healthcare arena, addressing the shortcomings of current diagnostic techniques. The CNN model was enhanced by integrating transfer learning methods and data augmentation using Magnetic Resonance (MR) images. The model's predictive performance was further improved by adding more training parameters in pre-trained deep learning models like ResNet-50, VGG-16, Inception V3, DenseNet201, Xception and MobileNet. According to experimental results, the suggested model performs better than baseline models and achieved a 99.40% classification accuracy rate.  According to this study, a more precise and effective way to diagnose brain tumors could be achieved in clinical settings by using the suggested model. The future direction suggested enhancing the dataset and further refining the model to improve its generalization capabilities in diverse clinical scenarios.