Data Mining Practical Machine Learning Tools and Techniques

to be done in this area. Face recognition is a problem that scarcely clamoured for attention before the computer age but, having surfaced, has involved a wide range of techniques and has attracted the attention of some fine minds (David Mumford was a Fields Medallist in 1974). This singular application of mathematical modelling to a messy applied problem of obvious utility and importance but with no unique solution is a pretty one to share with students: perhaps, returning to the source of our opening quotation, we may invert Duncan's earlier observation, 'There is an art to find the mind's construction in the face!'.

Linear and nonlinear waves, by G. B. Whitham. Pp.636. £50. 1999. ISBN 0 471 35942 4 (Wiley).

Introduction to Functional Analysis.

Artificial intelligence in retina.

A new analytical modelling for fractional telegraph equation via Laplace transform

A numerical algorithm for the solution of telegraph equations

Purpose
Detection (diagnosis) of diabetic retinopathy (DR) in optical coherence tomography (OCT) images for patients with type 2 diabetes, but almost clinically normal retina appearances.

Methods
The proposed computer-aided diagnostic (CAD) system detects the DR in three steps: (i) localizing and segmenting 12 distinct retinal layers on the OCT image; (ii) deriving features of the segmented layers, and (iii) learning most discriminative features and classifying each subject as normal or diabetic. To localise and segment the retinal layers, signals (intensities) of the OCT image are described with a joint Markov-Gibbs random field (MGRF) model of intensities and shape descriptors. Each segmented layer is characterized with cumulative probability distribution functions (CDF) of its locally extracted features, such as reflectivity, curvature, and thickness. A multistage deep fusion classification network (DFCN) with a stack of non-negativity-constrained autoencoders (NCAE) is trained to select the most discriminative retinal layers’ features and use their CDFs for detecting the DR. A training atlas was built using the OCT scans for 12 normal subjects and their maps of layers hand-drawn by retina experts.

Results
Preliminary experiments on 52 clinical OCT scans (26 normal and 26 with early-stage DR, balanced between 40{79 years old males and females; 40 training and 12 test subjects) gave the DR detection accuracy, sensitivity, and specificity of 92%; 83%, and 100%, respectively. The 100% accuracy, sensitivity, and specificity have been obtained in the leave-one-out cross-validation test for all the 52 subjects.

Conclusion
Both the quantitative and visual assessments confirmed the high accuracy of the proposed computer-assisted diagnostic system for early DR detection using the OCT retinal images.

This article is protected by copyright. All rights reserved.

A computer-aided diagnostic system for detecting diabetic retinopathy in optical coherence tomography images.

https://file.scirp.org/pdf/Engineering20100900005_72955125.pdf

Optimization of Biodynamic Seated Human Models Using Genetic Algorithms

2N order compact finite difference scheme with collocation method for solving the generalized Burger's-Huxley and Burger's-Fisher equations

Diabetic retinopathy (DR) is a progressive disease and its detection at an early stage is crucial for saving a patient's vision. In this paper, an enhanced computer-assisting diagnostic (CAD) system is developed for the discovery and grading of non-proliferative DR from optical coherence tomography (OCT) images. The proposed CAD system elaborates three sequential stages. Initially, 12 distinct retina layers are localized using our previously developed segmentation approach based on an integrated joint model that combines shape, intensity, and spatial information. Secondly, three features, namely the reflectivity, curvature, and thickness are quantitatively measured from the segmented layers. Finally, a two-stage deep fusion classification network (DFCN), trained by stacked non-negativity constraint autoencoder (SNCAE), is used first to classify the subject as normal or DR, then assess the grade of DR as either early stage or mild/moderate. Using "leave-one-subject-out" experiments on a dataset of 74 OCT images, the CAD system distinguished between normal and DR subjects with a 93% accuracy (sensitivity =91%, specificity =97%) and achieved a 98% correct classification between early stage and mild/moderate DR. These results confirm the proposed framework as a reliable non-invasive diagnostic tool.

M. S. El-Azab

Papers

A numerical algorithm for the solution of telegraph equations

A computer-aided diagnostic system for detecting diabetic retinopathy in optical coherence tomography images.

Optimization of Biodynamic Seated Human Models Using Genetic Algorithms

2N order compact finite difference scheme with collocation method for solving the generalized Burger's-Huxley and Burger's-Fisher equations

An integrated framework for automatic clinical assessment of diabetic retinopathy grade using spectral domain OCT images