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Medicine and Health science fair project:
Automated and accurate early-diagnosis of Alzheimer's disease




Science Fair Project Information
Title: Automated and accurate early-diagnosis of Alzheimer's disease
Subject: Medicine
Subcategory: Alzheimer's disease
Grade level: Middle School - Grades 7-9
Academic Level: Advanced
Project Type: Building / Engineering
Cost: Medium
Awards: Google Science Fair Finalist
Affiliation: Google Science Fair
Year: 2015
Materials: OASIS (Open Access Series of Imaging Studies), Artificial Neural Networks
Techniques: Mini Mental State Examination (MMSE),
Concepts: Computer-Aided Detection (CAD)
Description: The purpose of this project is to create an automated tool that could accurately diagnose Alzheimer's. The tool uses neural networks to train themselves to diagnose Alzheimer's. Image features from MRI scans of patients were extracted, and combined with clinical features to input into the neural networks. Then were tested three different structures of classifiers - a single stage neural network, chained neural networks, and hierarchical neural networks. The number of hidden neurons was varied, and different training algorithms were tried.
Link: https://www.googlesciencefair.com/projects/en/2015/66006cc
Short Background

Computer-Aided Detection (CAD)

In radiology, computer-aided detection (CAD) are procedures in medicine that assist doctors in the interpretation of medical images. Imaging techniques in X-ray, MRI, and Ultrasound diagnostics yield a great deal of information, which the radiologist has to analyze and evaluate comprehensively in a short time. CAD systems help scan digital images, e.g. from computed tomography, for typical appearances and to highlight conspicuous sections, such as possible diseases.

CAD is an interdisciplinary technology combining elements of artificial intelligence and computer vision with radiological image processing. CAD is used in the diagnosis of Pathological Brain Detection (PBD), breast cancer, lung cancer, colon cancer, prostate cancer, bone metastases, coronary artery disease, congenital heart defect, and Alzheimer's disease.

Magnetic resonance (MR) images are widely applied to help doctors and technicians for disease diagnosis because this imaging technique provides clearer soft tissue details without causing damages to the patient’s tissues. However, it is tedious and time-consuming for manual analysis, since the amount of data it associates is too large.

Hence, it is of necessity and urgency to develop automatic classification systems of MR brain images, which could perform with high accuracy rapidly in clinical medicine. This task is called "pathological brain detection (PBD)".

Pathological brain detection (PBD) Systems can be used to identify subjects with both Alzheimer's disease and mild cognitive impairment from healthy elder controls.

In machine learning and cognitive science, artificial neural networks (ANNs) are a family of models inspired by biological neural networks (the central nervous systems of animals, in particular the brain) which are used to estimate or approximate functions that can depend on a large number of inputs and are generally unknown.

For example, a neural network for handwriting recognition is defined by a set of input neurons which may be activated by the pixels of an input image. After being weighted and transformed by a function (determined by the network's designer), the activations of these neurons are then passed on to other neurons. This process is repeated until finally, the output neuron that determines which character was read is activated.

See also:
https://en.wikipedia.org/wiki/Computer-aided_diagnosis
https://en.wikipedia.org/wiki/Pathological_Brain_Detection_(PBD)

Source: Wikipedia (All text is available under the terms of the GNU Free Documentation License and Creative Commons Attribution-ShareAlike License.)

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