I'm PhD student in Machine learning (And Consultant in a global IT&T company full time) . I Live in Sydney Australia.
My research interests are sparse coding and applying nural language to practical application. Currently i'm doing research on image classification using various techniques to train deep networks for higher accuracy rate.
—Successful, timely diagnosis of neuropsychiatric diseases is key to management. Research efforts... more —Successful, timely diagnosis of neuropsychiatric diseases is key to management. Research efforts in the area of diagnosis of Alzheimer's disease have used various aspects of computer-aided multi-class diagnosis approaches with varied degrees of success. However, there is still need for more efficient and reliable approaches to successful diagnosis of the disease. This research used deep learning framework with modified k-sparse autoencoder (mKSA)classification to locate neutrally degenerated areas of the brain MRI, low amyloid beta 1-42 imaging in cerebrospinal fluid (CSF) and positron emission tomography (PET) imaging of amyloid; each with a sample of 150 images. Results show a correlation between computational demarcation of infected regions and the images. Degeneration in the studied areas was evidenced by high phosphorylated t-/p-tau levels in CSF, regional hypometabolism fluorodeoxyglucose PET, and the presence of atrophy patterns. The use of mKSA algorithm in boosting classification helped to improve the classifier performance. The KSA method with deep learning framework is used for the first time to produce accurate results in diagnosis of Alzheimer's disease.
—Successful, timely diagnosis of neuropsychiatric diseases is key to management. Research efforts... more —Successful, timely diagnosis of neuropsychiatric diseases is key to management. Research efforts in the area of diagnosis of Alzheimer's disease have used various aspects of computer-aided multi-class diagnosis approaches with varied degrees of success. However, there is still need for more efficient and reliable approaches to successful diagnosis of the disease. This research used deep learning framework with modified k-sparse autoencoder (mKSA)classification to locate neutrally degenerated areas of the brain MRI, low amyloid beta 1-42 imaging in cerebrospinal fluid (CSF) and positron emission tomography (PET) imaging of amyloid; each with a sample of 150 images. Results show a correlation between computational demarcation of infected regions and the images. Degeneration in the studied areas was evidenced by high phosphorylated t-/p-tau levels in CSF, regional hypometabolism fluorodeoxyglucose PET, and the presence of atrophy patterns. The use of mKSA algorithm in boosting classification helped to improve the classifier performance. The KSA method with deep learning framework is used for the first time to produce accurate results in diagnosis of Alzheimer's disease.
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