Chuandong Qin1,2, Yu Cao1,*, Liqun Meng1
CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1975-1994, 2024, DOI:10.32604/cmc.2024.049228
Abstract Brain tumors come in various types, each with distinct characteristics and treatment approaches, making manual detection a time-consuming and potentially ambiguous process. Brain tumor detection is a valuable tool for gaining a deeper understanding of tumors and improving treatment outcomes. Machine learning models have become key players in automating brain tumor detection. Gradient descent methods are the mainstream algorithms for solving machine learning models. In this paper, we propose a novel distributed proximal stochastic gradient descent approach to solve the L-Smooth Support Vector Machine (SVM) classifier for brain tumor detection. Firstly, the smooth hinge loss is… More >
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