scholarly journals An Optimized Method Using CNN, RF, Cuckoo Search and HOG for Early Detection of Eye Disease in Humans

2020 ◽  
Vol 4 (2) ◽  
Author(s):  
Tian Jipeng ◽  
Manasa S. ◽  
T. C. Manjunath
Keyword(s):  
Neural Network ◽  
Early Detection ◽  
Hybrid Algorithm ◽  
Cuckoo Search ◽  
Eye Diseases ◽  
Image Texture ◽  
Eye Disease ◽  
Advanced Stage ◽  
Permanent Damage ◽  
Unique Method

Glaucoma is a group of eye diseases that cause damage to the optic nerve, causing the successive narrowing of the visual field in affected patients due to increased intraocular pressure, which can lead the patient, at an advanced stage, to blindness without clinical reversal. As we have heard and seen from generations across that Glaucoma has been and is still one of the leading diseases that has permanent damage if untreated. As per the current research it says that 79 Million are affected BY 2020 which are untreated. So, to make it easy for us humans, early detection is one of the best way to create awareness and treat the diseased. After having gone through the majority of the literatures, have seen that when LBP is given to HOG has accurate results for better feature extraction than other methods, also application of Cuckoo search (CS) algorithm, Random forest (for classifying) and Conventional Neural Network (for segmentation) have better outcome compared to the previously used hybrid algorithm methods to detected the diseased from the normal eye. So, to achieve this I will be using Matlab tool as it produces more accurate results than any other platform. In one of the paper LBP algorithm has been extensively used to obtain the desired results but when learnt about HOG, it looked as it has better properties to enhance the required results when combined along with LBP. CS is another unique method to analyze on aggregation of the image texture.

Glaucoma Detection Using Convolutional Neural Networks

2021 ◽  
pp. 1-7
Author(s):  
Meet Ganpatlal Oza ◽  
Geeta Rani ◽  
Vijaypal Singh Dhaka
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Intraocular Pressure ◽  
Early Detection ◽  
Early Stage ◽  
Physical Activities ◽  
Eye Disease ◽  
Eye Injuries ◽  
Glaucoma Detection ◽  
Ict Tools

The increase in use of ICT tools and decrease in physical activities has increased the risk of disorders such as diabetes, hypertension, myopia, hypermetropia, etc. These disorders make the person more prone to eye disease such as glaucoma. The actual causes of glaucoma are still unknown. But the study of medical literature reveals that the factors such as intraocular pressure, thyroid, diabetics, eye injuries, eye surgeries, ethnic background, and myopia makes the person more prone to glaucoma. The difficulty in early detection make it an invisible thief of sight. Therefore, it is the demand of the day to design a system for its early detection. The aim of this chapter is to develop a convolutional neural network model “GlaucomaDetector” for detection of glaucoma at an early stage. The evaluation of the model on the publicly available dataset reports the accuracy of 99% for prediction of glaucoma from the input images of retina. This may prove a useful tool for doctors for quick prediction of glaucoma at an early stage. Thus, it can minimize the risk of blindness in patients.

scholarly journals Early Detection of Alzheimer’s Disease Using Polar Harmonic Transforms and Optimized Wavelet Neural Network

2021 ◽  
Vol 11 (4) ◽  
pp. 1574
Author(s):  
Shabana Urooj ◽  
Satya P. Singh ◽  
Areej Malibari ◽  
Fadwa Alrowais ◽  
Shaeen Kalathil
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Detection ◽  
Differential Evolution ◽  
Early Stage ◽  
Selection Process ◽  
Parameter Tuning ◽  
Wavelet Neural Network ◽  
Tuning Parameter

Effective and accurate diagnosis of Alzheimer’s disease (AD), as well as early-stage detection, has gained more and more attention in recent years. For AD classification, we propose a new hybrid method for early detection of Alzheimer’s disease (AD) using Polar Harmonic Transforms (PHT) and Self-adaptive Differential Evolution Wavelet Neural Network (SaDE-WNN). The orthogonal moments are used for feature extraction from the grey matter tissues of structural Magnetic Resonance Imaging (MRI) data. Irrelevant features are removed by the feature selection process through evaluating the in-class and among-class variance. In recent years, WNNs have gained attention in classification tasks; however, they suffer from the problem of initial parameter tuning, parameter setting. We proposed a WNN with the self-adaptation technique for controlling the Differential Evolution (DE) parameters, i.e., the mutation scale factor (F) and the cross-over rate (CR). Experimental results on the Alzheimer’s disease Neuroimaging Initiative (ADNI) database indicate that the proposed method yields the best overall classification results between AD and mild cognitive impairment (MCI) (93.7% accuracy, 86.0% sensitivity, 98.0% specificity, and 0.97 area under the curve (AUC)), MCI and healthy control (HC) (92.9% accuracy, 95.2% sensitivity, 88.9% specificity, and 0.98 AUC), and AD and HC (94.4% accuracy, 88.7% sensitivity, 98.9% specificity and 0.99 AUC).

scholarly journals Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 742
Author(s):  
Canh Nguyen ◽  
Vasit Sagan ◽  
Matthew Maimaitiyiming ◽  
Maitiniyazi Maimaitijiang ◽  
Sourav Bhadra ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Early Detection ◽  
Convolutional Neural Network ◽  
Near Infrared ◽  
Hyperspectral Data ◽  
Viral Diseases ◽  
Support Vector ◽  
Spectral Features ◽  
Feature Spaces

Early detection of grapevine viral diseases is critical for early interventions in order to prevent the disease from spreading to the entire vineyard. Hyperspectral remote sensing can potentially detect and quantify viral diseases in a nondestructive manner. This study utilized hyperspectral imagery at the plant level to identify and classify grapevines inoculated with the newly discovered DNA virus grapevine vein-clearing virus (GVCV) at the early asymptomatic stages. An experiment was set up at a test site at South Farm Research Center, Columbia, MO, USA (38.92 N, −92.28 W), with two grapevine groups, namely healthy and GVCV-infected, while other conditions were controlled. Images of each vine were captured by a SPECIM IQ 400–1000 nm hyperspectral sensor (Oulu, Finland). Hyperspectral images were calibrated and preprocessed to retain only grapevine pixels. A statistical approach was employed to discriminate two reflectance spectra patterns between healthy and GVCV vines. Disease-centric vegetation indices (VIs) were established and explored in terms of their importance to the classification power. Pixel-wise (spectral features) classification was performed in parallel with image-wise (joint spatial–spectral features) classification within a framework involving deep learning architectures and traditional machine learning. The results showed that: (1) the discriminative wavelength regions included the 900–940 nm range in the near-infrared (NIR) region in vines 30 days after sowing (DAS) and the entire visual (VIS) region of 400–700 nm in vines 90 DAS; (2) the normalized pheophytization index (NPQI), fluorescence ratio index 1 (FRI1), plant senescence reflectance index (PSRI), anthocyanin index (AntGitelson), and water stress and canopy temperature (WSCT) measures were the most discriminative indices; (3) the support vector machine (SVM) was effective in VI-wise classification with smaller feature spaces, while the RF classifier performed better in pixel-wise and image-wise classification with larger feature spaces; and (4) the automated 3D convolutional neural network (3D-CNN) feature extractor provided promising results over the 2D convolutional neural network (2D-CNN) in learning features from hyperspectral data cubes with a limited number of samples.

Image Texture Analysis based on Color Level Methods using Neural Network

2020 ELEKTRO ◽  
2020 ◽  
Author(s):  
Miroslav Benco ◽  
Patrik Kamencay ◽  
Robert Hudec ◽  
Martina Radilova ◽  
Peter Sykora
Keyword(s):  
Neural Network ◽  
Texture Analysis ◽  
Image Texture ◽  
Image Texture Analysis

scholarly journals Classification of ETM+ Remote Sensing Image Based on Hybrid Algorithm of Genetic Algorithm and Back Propagation Neural Network

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Haisheng Song ◽  
Ruisong Xu ◽  
Yueliang Ma ◽  
Gaofei Li
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Genetic Algorithm ◽  
Hybrid Algorithm ◽  
Local Minimum ◽  
Back Propagation ◽  
Remote Sensing Image ◽  
Back Propagation Neural Network ◽  
Bp Algorithm ◽  
Improved Bp Algorithm

The back propagation neural network (BPNN) algorithm can be used as a supervised classification in the processing of remote sensing image classification. But its defects are obvious: falling into the local minimum value easily, slow convergence speed, and being difficult to determine intermediate hidden layer nodes. Genetic algorithm (GA) has the advantages of global optimization and being not easy to fall into local minimum value, but it has the disadvantage of poor local searching capability. This paper uses GA to generate the initial structure of BPNN. Then, the stable, efficient, and fast BP classification network is gotten through making fine adjustments on the improved BP algorithm. Finally, we use the hybrid algorithm to execute classification on remote sensing image and compare it with the improved BP algorithm and traditional maximum likelihood classification (MLC) algorithm. Results of experiments show that the hybrid algorithm outperforms improved BP algorithm and MLC algorithm.

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