Zeyad Ghaleb Al-Mekhlafi¹, Ebrahim Mohammed Senan², Taha H. Rassem³, Badiea Abdulkarem Mohammed^4,5,*, Nasrin M. Makbol⁵, Adwan Alownie Alanazi¹, Tariq S. Almurayziq¹, Fuad A. Ghaleb⁶

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 775-796, 2022, DOI:10.32604/cmc.2022.024492 - 24 February 2022

Abstract Stroke and cerebral haemorrhage are the second leading causes of death in the world after ischaemic heart disease. In this work, a dataset containing medical, physiological and environmental tests for stroke was used to evaluate the efficacy of machine learning, deep learning and a hybrid technique between deep learning and machine learning on the Magnetic Resonance Imaging (MRI) dataset for cerebral haemorrhage. In the first dataset (medical records), two features, namely, diabetes and obesity, were created on the basis of the values of the corresponding features. The t-Distributed Stochastic Neighbour Embedding algorithm was applied to… More >

Yuhua Gong¹, Shilei Hao^1,*, Bochu Wang^1,*

Molecular & Cellular Biomechanics, Vol.16, Suppl.2, pp. 143-143, 2019, DOI:10.32604/mcb.2019.07095

Abstract The mechanical response of brain tissue closely relates to cerebral blood flow and brain diseases. During intracerebral haemorrhage (ICH), a mass effect occurs during the initial bleeding. As the hematoma increases, the haematoma mass effect continues to squeeze the brain tissue mechanically, which can even lead to the formation of fatal cerebral hernia. However, fewer studies have focused on the brain damage mechanisms and treatment approaches associated with mass effects compared to the secondary brain injuries after ICH, which may be a result of the absence of acceptable animal models mimicking a mass effect. Thus,… More >