Early Detection And Multi Classification Of Vascular Dementia Using Deep Learning

Project Title

Early Detection And Multi Classification Of Vascular Dementia Using Deep Learning

Project Area of Specialization Artificial IntelligenceProject Summary

Vascular Dementia is a disease related to loss of memory and intellectual abilities that interfere the daily life such as the problem with planning, organizing, decision-making, and confusion with time or space. Vascular dementia (VD) may occur as a result of cerebrovascular disease in which some regions of the brain have reduced or non-existent blood supply to the brain. VD is also the result of several strokes; approximately a fifth of individuals undergoing a stroke are expected to develop difficulties affecting their mental abilities In Pakistan alone, an estimated 150,000-200,000 dementia patients at present. The early detection and multi-classification of Vascular Dementia and proper care could reduce the risk of the disease. But only 10 percent of the cases who suffer from this disease are diagnosed early by medical experts in developing countries like Pakistan. In our country, loss of memory and intellectual abilities is considered an aging factor and there is a serious lack of research and knowledge regarding this disease. Even though the Vascular Dementia growth rate is alarming in Pakistan, very little research effort is focused on issues related to it.

In this research, the primary objective is to develop a deep learning-based architecture using neuro-imaging data for computer-aided early diagnosis (CAD) of Vascular Dementia. Standard benchmark datasets will help in fine-tuning the system. Moreover, the coordination with local hospitals and laboratories in Pakistan will be an integral part of the research which shall facilitate the neurologists in the early diagnosis of Vascular Dementia disease. Further refinement will be to categorize Vascular Dementia in different stages. State-of-the-art deep learning methods will be employed to achieve the high performance of this CAD system. The secondary objective of the proposed research-based implementation model is to create awareness among people about this disease. The research and development in this area will improve the health-care facilities of Pakistani medical institutions.

Project Objectives

The main objective is to propose a fast, reliable, and efficient Deep Learning-based Computer-aided diagnosis of Vascular Dementia.

1. The scope of this project is early diagnosis of VD, with the clear multi-classification of the severity of the disease.
2. To get higher accuracy in computer-aided diagnosis (CAD) as compared to other states of the art techniques.
3. To investigate current clinical practices in the identification and intervention for Vascular Dementia.
4. To explore the development of computer-aided diagnosis for VD in Pakistan.
5. A secondary objective of the project is to create awareness among people who have a chance to become the victim of VD by early diagnosis, so the affected people and their caretakers should take preliminary measures to control the severity of the disease.

Project Implementation Method

In this research, the primary objective is to develop a deep learning-based architecture using neuro-imaging data for computer-aided early diagnosis (CAD) of Vascular Dementia. Standard benchmark datasets will help in fine-tuning the system. Moreover, the coordination with local hospitals and laboratories in Pakistan (Rahila Research & Reference Lab) will be an integral part of the research which shall facilitate the neurologists in the early diagnosis of Vascular Dementia. Further refinement will be to categorize Vascular Dementia in different stages. State-of-the-art deep learning methods will be employed to achieve the high performance of this CAD system. The secondary objective of the proposed research-based implementation model is to create awareness among people about this disease. The research and development in this area will improve the health-care facilities of Pakistani medical institutions.

Benefits of the Project

There are Following Benefits of this projects are as follows:

1. The foremost expected benefit of this research-based implementation project is higher accuracy in computer-aided diagnosis of Vascular Dementia at an early stage to lower the severity of the disease
2. We expect the outcome model of this research will be a second opinion alongside neurologists for the early diagnosis of Vascular Dementia.
3. This will help to create awareness about Vascular Dementia among people and caretakers of VD patients.

Technical Details of Final Deliverable

                   Figure  1: Block Diagram Of Proposed Model

In our proposed research, we will use a deep learning architecture, consisting of stacked sparse autoencoders and a softmax regression layer. This proposed method will work for multi-class classification. This will not only classify VD but also predict the risk of Mild cognitive impairment (MCI). Prediction is categorized into four classes, Vascular Dementia (VD), normal control (NC), MCI non-converters (ncMCI), and MCI converters (cMCI). The sparse autoencoder is an encoding structure, which consists of a neural network with multiple hidden layers. The proposed model will be trained and test using a Graphical Processing Unit (GPU).

Final Deliverable of the Project HW/SW integrated systemCore Industry MedicalOther Industries Health Core Technology Artificial Intelligence(AI)Other Technologies NeuroTech, OthersSustainable Development Goals Good Health and Well-Being for PeopleRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	61950
Gigabyte Z390X AORUS ULTRA LGA	Equipment	1	40450	40450
12 V adapters	Equipment	4	500	2000
ADATA SSD 256GB M.2 NVME	Equipment	1	9500	9500
Cloud Services Licensing	Miscellaneous	1	8000	8000
Cable Wires	Miscellaneous	1	2000	2000