COMPUTER-AIDED EARLY DIAGNOSIS OF ALZHEIMER’S DISEASE BASED ON COST-SENSITIVE AND DEEP RESIDUAL CONVOLUTIONAL NEURAL NETWORK

Abstract

Alzheimer’s disease (AD) is a neurodegenerative illness of unclear pathogenic origin that is characterized in its early stages by a steady deterioration in memory and cognitive impairment. Mild cognitive impairment (MCI) is a preclinical stage of AD. If a patient is diagnosed with early MCI, he or she can take preventive actions before permanent brain damage occurs. Improper diagnosis of MCI may cause the patient to miss the best time for treatment and incur heavy cost. Cost-sensitivity refers to the significant difference in cost to the patient between a misdiagnosis of MCI as normal control (NC) or AD and a misdiagnosis of NC or AD as MCI. In order to give consideration to both accuracy and cost, a computer-aided diagnosis algorithm based on cost-sensitive, attention mechanism and deep residual convolutional neural network (CSAResnet) was proposed for AD early diagnosis from MRI images. MRI data were obtained from the open-access AD Neuroimaging Initiative (ADNI) database. The experimental results show that the CSAResnet algorithm can balance the reduction of the total misclassification cost and the improvement of the accuracy to distinguish AD and MCI patients from NC subjects. It can address multiple classes of cost-sensitive problems.