Fast brain tumor detection using adaptive stochastic gradient descent on shared-memory parallel environment

Brain tumor detection is a very important and challenging task. An efficient detection algorithm is of great importance to the practice of brain tumor medicine. In this paper, we propose a novel parallel optimization algorithm based on a shared-memory environment to solve the SVM classifier for brain tumor detection. Firstly, the HOG algorithm is used to extract brain tumor MR image features and compare them with the wavelet transform method. Secondly, SVM with symbolscript loss function is utilized as a classifier. Due to its sparsity, the detection speed is significantly faster. Finally, SMP-SGD, SMP-Momentum, SMP-Adagrad, and SMP-Adam algorithms are proposed and applied to the classifier solution. The experimental results show that the HOG algorithm extracts brain tumor MRI features more effectively than the discrete wavelet transform method. The proposed SMP-SGD algorithm and its variants achieved state-of-the-art accuracy and efficiency for brain tumor detection.