State-of-the-Art CNN Optimizer for Brain Tumor Segmentation in Magnetic Resonance Images

Abstract

<jats:p>Brain tumors have become a leading cause of death around the globe. The main reason for this epidemic is the difficulty conducting a timely diagnosis of the tumor. Fortunately, magnetic resonance images (MRI) are utilized to diagnose tumors in most cases. The performance of a Convolutional Neural Network (CNN) depends on many factors (i.e., weight initialization, optimization, batches and epochs, learning rate, activation function, loss function, and network topology), data quality, and specific combinations of these model attributes. When we deal with a segmentation or classification problem, utilizing a single optimizer is considered weak testing or validity unless the decision of the selection of an optimizer is backed up by a strong argument. Therefore, optimizer selection processes are considered important to validate the usage of a single optimizer in order to attain these decision problems. In this paper, we provides a comprehensive comparative analysis of popular optimizers of CNN to benchmark the segmentation for improvement. In detail, we perform a comparative analysis of 10 different state-of-the-art gradient descent-based optimizers, namely Adaptive Gradient (Adagrad), Adaptive Delta (AdaDelta), Stochastic Gradient Descent (SGD), Adaptive Momentum (Adam), Cyclic Learning Rate (CLR), Adaptive Max Pooling (Adamax), Root Mean Square Propagation (RMS Prop), Nesterov Adaptive Momentum (Nadam), and Nesterov accelerated gradient (NAG) for CNN. The experiments were performed on the BraTS2015 data set. The Adam optimizer had the best accuracy of 99.2% in enhancing the CNN ability in classification and segmentation.</jats:p>

Deep Learning Techniques for the Classification of Colorectal Cancer Tissue

“…For example, the weight initialization, batch sizes, epochs, learning rates, activation function, optimizer, loss function, network topology, etc. The optimizer selection study of [27] for brain tumor segmentation in magnetic resonance images (MRI) suggests that a good optimizer could be a critical issue for the proposed approach. The authors of [27] listed 10 different state-of-the-art optimizer including: adaptive gradient (Adagrad), adaptive delta (AdaDelta), stochastic gradient descent (SGD), adaptive momentum (Adam), cyclic learning rate (CLR), adaptive max pooling (Adamax), root mean square propagation (RMS Prop), Nesterov adaptive momentum (Nadam), and Nesterov accelerated gradient (NAG) for CNN.…”

“…The authors of [27] listed 10 different state-of-the-art optimizer including: adaptive gradient (Adagrad), adaptive delta (AdaDelta), stochastic gradient descent (SGD), adaptive momentum (Adam), cyclic learning rate (CLR), adaptive max pooling (Adamax), root mean square propagation (RMS Prop), Nesterov adaptive momentum (Nadam), and Nesterov accelerated gradient (NAG) for CNN. The Adam optimizer achieved the best accuracy in study of [27] for MRI. Comprehensive analyses have been performed during this study for those optimizers.…”

“…pooling (Adamax), root mean square propagation (RMS Prop), Nesterov adaptive momentum (Nadam), and Nesterov accelerated gradient (NAG) for CNN. The Adam optimizer achieved the best accuracy in study of[27] for MRI. Comprehensive analyses have been performed during this study for those optimizers.…”

Multi-Classification of Brain Tumor MRI Images Using Deep Convolutional Neural Network with Fully Optimized Framework

“…Brain tumor classification using machine learning methods has previously been studied by researchers especially over the past years. The development of artificial intelligence and deep learning-based new technologies has made a great impact in the field of medical image analysis, especially in the field of disease diagnosis (Mehmood et al 2020 , 2021 ; Yaqub et al 2020 ). Parallel to this, many studies have been conducted on brain tumor detection and brain tumor multi-classification using CNN.…”

A New Hybrid Optimizer for Global Optimization Based on a Comparative Study Remarks of Classical Gradient Descent Variants

“…Momentum is a method that helps accelerate Figure 1. Example of SGD running -case of the ackley function SGD in the relevant direction and dampens oscillations [11] [17] [25] .…”