Wavelet-Based Energy Features for Glaucomatous Image Classification

Dua, Sumeet; Acharya, U. Rajendra; Chowriappa, Pradeep; Sree, S. Vinitha

doi:10.1109/titb.2011.2176540

Cited by 224 publications

(120 citation statements)

References 18 publications

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“…Sensitivity, specificity, and area under receiver operating characteristic curve (AUROC) were assessed. Dua S. et al [3] dexterously conducted relentless research on the biased latent of wavelet traits gathered from the daubechies (db3), symlets (sym3), and biorthogonal (bio3.3, bio3.5, and bio3.7) wavelet filters. They brought to light a method to mine energy signatures attained by the use of 2-D discrete wavelet transform, and subject these signatures to various trait levels and trait choice approaches.…”

Section: Literature Surveymentioning

confidence: 99%

“…In the random walk of the j th scrounger, this is performed by supplementing an arbitrary locational value of the producers, which is easily gathered from the set of Y values from the producers, as defined by Eq. (8), 3 ( )…”

Section: Gso Work-flowmentioning

confidence: 99%

“…where, 3 n is a uniform random sequence in the range (0, 1). Operator "•" is the Hadamard product which computes the entry wise product of the two vectors.…”

Section: Gso Work-flowmentioning

confidence: 99%

“…In WT, the image is symbolized in terms of the frequency of content of limited regions over a variety of scales. This illustration furnishes a structure for the scrutiny of image traits, which are autonomous in dimension and can regularly be characterized by their frequency domain traits [3].…”

Section: Introductionmentioning

confidence: 99%

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Optimal Hyper Analytic Wavelet Transform for Glaucoma Detection in Fundal Retinal Images

Raja¹,

Gangatharan²

2015

Journal of Electrical Engineering and Technology

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-Glaucoma is one of the most common causes of blindness which is caused by increase of fluid pressure in the eye which damages the optic nerve and eventually causing vision loss. An automated technique to diagnose glaucoma disease can reduce the physicians' effort in screening of Glaucoma in a person through the fundal retinal images. In this paper, optimal hyper analytic wavelet transform for Glaucoma detection technique from fundal retinal images is proposed. The optimal coefficients for transformation process are found out using the hybrid GSO-Cuckoo search algorithm. This technique consists of pre-processing module, optimal transformation module, feature extraction module and classification module. The implementation is carried out with MATLAB and the evaluation metrics employed are accuracy, sensitivity and specificity. Comparative analysis is carried out by comparing the hybrid GSO with the conventional GSO. The results reported in our paper show that the proposed technique has performed well and has achieved good evaluation metric values. Two 10-fold cross validated test runs are performed, yielding an average fitness of 91.13% and 96.2% accuracy with CGD-BPN (Conjugate Gradient Descent-Back Propagation Network) and Support Vector Machines (SVM) respectively. The techniques also gives high sensitivity and specificity values. The attained high evaluation metric values show the efficiency of detecting Glaucoma by the proposed technique.

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Section: Literature Surveymentioning

confidence: 99%

Section: Gso Work-flowmentioning

confidence: 99%

“…where, 3 n is a uniform random sequence in the range (0, 1). Operator "•" is the Hadamard product which computes the entry wise product of the two vectors.…”

Section: Gso Work-flowmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal Hyper Analytic Wavelet Transform for Glaucoma Detection in Fundal Retinal Images

Raja¹,

Gangatharan²

2015

Journal of Electrical Engineering and Technology

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“…They used classifiers such as Naive Based Classifier, k-Nearest Neighbor Classifier and Support Vector Machines (SVM) [49] to classify between healthy and Glaucomatous images and they found SVM to be less prone to sparsely sampled feature space. Dua et al [94] used Wavelet-Based Energy Features and compared the performance of different classifiers including the previously mentioned classifiers, Random Forests [98] and Sequential minimal optimization (SMO) [99]. From SMO they obtained the maximum classification accuracy.…”

Section: Non-segmentation Based Classification Between Normal and Glamentioning

confidence: 99%

Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: A review

Haleem

Han

Hemert³

et al. 2013

Computerized Medical Imaging and Graphics

146

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Deep learning in medical imaging: A brief review

Serte¹,

Serener²,

Al‐Turjman³

2020

Trans Emerging Tel Tech

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Researchers have used deep learning methods for a human level or better disease identification and detection. This paper reports, in brief, the recent work in deep learning identification of diseases occurring at three unique parts of the human body: the skin, the thorax, and the eye. While earlier reviews reported on the theory, applications, and challenges of such research, what distinguishes this work from the others is the reporting and comprehensive analysis of the key results. In doing so, the paper not only summarizes the major conclusions drawn from them but also emphasizes their weaknesses. The hope is to help the researchers see the big picture in deep learning classification of the diseases of the skin, the thorax and the eye, and guide them to find the right future research direction. INTRODUCTIONPhysicians use different imaging modality techniques, such as MRI, CT, ultrasound, and X-ray to help them detect, diagnose, and cure serious diseases. With help from radiologists, they can interpret a vast number of diseases occurring at distinct parts of a human body, ranging from brain to lungs to skin. Still, the tremendous diversity in disease pathology can make human errors possible.In search of computer methods to aid in disease detection and recognition, researchers have experimented on convolutional neural networks (CNN) since the mid-90s. 1 Used for hand-written digit recognition at first, 2 CNN's popularity increased with the discovery of deep CNNs in 2012. [3][4][5][6][7][8] Since then, a growing number of researchers are using CNNs for medical image analysis (such as References 9-17). Some even show that CNNs can give comparable performance to medical doctors. 18 We may use the Internet of Things (IoT) to connect medical devices in hospitals for the collection of big data. Each hospital can join a central unit this way for the automated detection of diseases on images using CNN. IoT is a crucial tool for reaching data in an instant, and artificial intelligence is essential for modeling for disease detection on images. Together they may form what we call The Internet of Medical Things (IoMT). 19 CNN's distinct architectures and vast application areas (such as this and References 20-22) have likewise led the research groups to publish a few review papers on using deep CNNs in medical imaging (eg,. While these papers focus on the theory, applications, and challenges of deep CNNs, they do not detail the results discovered by the researchers.This survey paper, yet, aims to fill that gap by reporting the recent advances of deep learning use in medical imaging and analyzing the key results. In doing that, it hopes to offer the reader an opportunity to see the big picture, including any strengths and weaknesses that such research might have. The intention is not to cover the entire application areas in medical imaging but focus on only a few parts of the human body and be thorough. Hence, this survey paper includes

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Wavelet-Based Energy Features for Glaucomatous Image Classification

Cited by 224 publications

References 18 publications

Optimal Hyper Analytic Wavelet Transform for Glaucoma Detection in Fundal Retinal Images

Optimal Hyper Analytic Wavelet Transform for Glaucoma Detection in Fundal Retinal Images

Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: A review

Deep learning in medical imaging: A brief review

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