Deep Learning with EfficientNetB1 for detecting brain tumors in MRI images

Abstract

Machine learning (ML) and computer vision system revolutionized the world, especially Deep learning (DL) for convolutional neural networks, which has proven breakthroughs in brain tumor (BT) diagnosis. This study investigates a Convolutional Neural Network CNN approach for image classification for BT detection using the EfficientNetBl architecture with Global Average Pooling (GAP) layers in a big data setting. A classification layer is done with a softMax layer. The system is created in the Apache Spark environment. Spark system is a unified and ultra-fast analysis engine for large-scale data processing. It is mainly dedicated to Big Data and DL. Experiments are carried out using the brain magnetic resonance imaging dataset containing 3264 MRI scans to predict the performance of the model. The dataset is decomposed into training and testing datasets. The model’s performance was assessed and compared to existing models, it yielded a high precision, precision, fl-score, and weighted average. In our work, we have obtained an accuracy of 97% and a performance of 98% on a dataset of 3064 brain MRI images.