An approach to classification of data with highly localized unmarked features using neural networks

Grzeszczuk, Rafał Wojciech

doi:10.7494/csci.2019.20.3.3343

Cited by 2 publications

(3 citation statements)

References 13 publications

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“…Structure tensor [18] are depicted by M × M symmetric matrices which comprises of structural details about pixel intensity and orientation. Initially, the matrix was evaluated from the gradients of grayscale image I using the gradient tensors (10) In which, I x ; I y are spatial derivatives along the direction of x and y of image, I. But det G ð Þ ¼ 0, the gradient tensor will have only one non-zero eigen value, and respective eigen vector v ¼ 2 I x I y 2I y 2 À Á 2…”

Section: Structure Tensormentioning

confidence: 99%

“…Significant results were yielded and also comparison was made with naive bayes classifier to prove the accuracy of classifiers used in research. Grzeszczuk [10], proposed a new method to classify datasets with sparse high localized features by applying saliency map for enhancement of features. This method did not apply any pre-information feature localization.…”

Section: Introductionmentioning

confidence: 99%

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A Convolutional Neural Network Classifier VGG-19 Architecture for Lesion Detection and Grading in Diabetic Retinopathy Based on Deep Learning

Sudha¹,

Ganeshbabu²

2020

Computers, Materials &Amp; Continua

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Diabetic Retinopathy (DR) is a type of disease in eyes as a result of a diabetic condition that ends up damaging the retina, leading to blindness or loss of vision. Morphological and physiological retinal variations involving slowdown of blood flow in the retina, elevation of leukocyte cohesion, basement membrane dystrophy, and decline of pericyte cells, develop. As DR in its initial stage has no symptoms, early detection and automated diagnosis can prevent further visual damage. In this research, using a Deep Neural Network (DNN), segmentation methods are proposed to detect the retinal defects such as exudates, hemorrhages, microaneurysms from digital fundus images and then the conditions are classified accurately to identify the grades as mild, moderate, severe, no PDR, PDR in DR. Initially, saliency detection is applied on color images to detect maximum salient foreground objects from the background. Next, structure tensor is applied powerfully to enhance the local patterns of edge elements and intensity changes that occur on edges of the object. Finally, active contours approximation is performed using gradient descent to segment the lesions from the images. Afterwards, the output images from the proposed segmentation process are subjected to evaluate the ratio between the total contour area and the total true contour arc length to label the classes as mild, moderate, severe, No PDR and PDR. Based on the computed ratio obtained from segmented images, the severity levels were identified. Meanwhile, statistical parameters like the mean and the standard deviation of pixel intensities, mean of hue, saturation and deviation clustering, are estimated through K-means, which are computed as features from the output images of the proposed segmentation process. Using these derived feature sets as input to the classifier, the classification of DR was performed. Finally, a VGG-19 deep neural network was trained and tested using the derived feature sets from the KAGGLE fundus image dataset containing 35,126 images in total. The VGG-19 is trained with features extracted from 20,000 images and tested with features extracted from 5,000 images to achieve a sensitivity of 82% and an accuracy of 96%. The proposed system was able to label and classify DR grades automatically.

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Section: Structure Tensormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Convolutional Neural Network Classifier VGG-19 Architecture for Lesion Detection and Grading in Diabetic Retinopathy Based on Deep Learning

Sudha¹,

Ganeshbabu²

2020

Computers, Materials &Amp; Continua

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“…Several methods for image deformation have been developed and applied to the distinct fields of mathematics and computer science since 1992. These methods include artificial intelligence [11], image encryption [12], graph theory, [13] and many more [14][15][16][17][18][19][20][21][22][23][24][25][26]. The adoption of diffeomorphic image registration has caused a revolutionary and significant change in the field.…”

Section: Introductionmentioning

confidence: 99%

Image Registration using the Rigid Group

Tufail

Gul

2023

Sci Inquiry Rev.

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Image registration is the process of approximate matching of the source image to the target so that they resemble each other. In this study, two-dimensional image registration is presented using the rigid group. This group is a finite dimensional group (four-dimensional in this case) under composition. The dimensions of the rigid group are scaling, rotation, and translations along the axes. In this paper, an algorithm for the construction of rigid transformation is presented using the discretized objective function. This objective function is based on SSD (sum of the squares of the distances between the pixels intensities) and calculates the discrepancy between the images. The coarse search and the gradient descent approaches have been used for the optimization. The proposed algorithm is implemented on variety of images. The numerical examples illustrate the ability of the proposed algorithm.

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An approach to classification of data with highly localized unmarked features using neural networks

Cited by 2 publications

References 13 publications

A Convolutional Neural Network Classifier VGG-19 Architecture for Lesion Detection and Grading in Diabetic Retinopathy Based on Deep Learning

A Convolutional Neural Network Classifier VGG-19 Architecture for Lesion Detection and Grading in Diabetic Retinopathy Based on Deep Learning

Image Registration using the Rigid Group

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