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Alzheimer Disease Detection Empowered with Transfer Learning 被引量:3
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作者 Taher M.Ghazal Sagheer Abbas +6 位作者 Sundus Munir M.A.Khan Munir Ahmad Ghassan F.Issa Syeda Binish Zahra Muhammad Adnan Khan Mohammad Kamrul Hasan 《Computers, Materials & Continua》 SCIE EI 2022年第3期5005-5019,共15页
Alzheimer’s disease is a severe neuron disease that damages brain cells which leads to permanent loss of memory also called dementia.Many people die due to this disease every year because this is not curable but earl... Alzheimer’s disease is a severe neuron disease that damages brain cells which leads to permanent loss of memory also called dementia.Many people die due to this disease every year because this is not curable but early detection of this disease can help restrain the spread.Alzheimer’s ismost common in elderly people in the age bracket of 65 and above.An automated system is required for early detection of disease that can detect and classify the disease into multiple Alzheimer classes.Deep learning and machine learning techniques are used to solvemanymedical problems like this.The proposed system Alzheimer Disease detection utilizes transfer learning on Multi-class classification using brain Medical resonance imagining(MRI)working to classify the images in four stages,Mild demented(MD),Moderate demented(MOD),Non-demented(ND),Very mild demented(VMD).Simulation results have shown that the proposed systemmodel gives 91.70%accuracy.It also observed that the proposed system gives more accurate results as compared to previous approaches. 展开更多
关键词 Convolutional neural network(CNN) alzheimer’s disease(AD) medical resonance imagining mild demented
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