New approach for objective cataract classification based on ultrasound techniques using multiclass SVM classifiers

Caixinha, Miguel; Velte, Elena; Santos, Mário; Santos, Jaime

doi:10.1109/ultsym.2014.0599

Cited by 20 publications

(7 citation statements)

References 28 publications

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“…Ultrasound Images & Digital Camera Images. For ultrasound images, researchers adopt the Fourier Transform (FT) method, textural analysis method, and probability density to extract features [6,8,7]. The procedures to extract features from digital camera images is the same to slit lamp images, but different image processing methods are used such as Gabor Wavelet transform, Gray level Co-occurrence Matrix (GLCM), image morphological feature, and Gaussian filter [87,33,86,34].…”

Section: Feature Extractionmentioning

confidence: 99%

Machine Learning for Cataract Classification and Grading on Ophthalmic Imaging Modalities: A Survey

Zhang¹,

Fang²,

Hu³

et al. 2020

Preprint

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Cataract is one of the leading causes of reversible visual impairment and blindness globally. Over the years, researchers have achieved significant progress in developing state-of-the-art artificial intelligence techniques for automatic cataract classification and grading, helping clinicians prevent and treat cataract in time. This paper provides a comprehensive survey of recent advances in machine learning for cataract classification and grading based on ophthalmic images. We summarize existing literature from two research directions: conventional machine learning techniques and deep learning techniques. This paper also provides insights into existing works of both merits and limitations. In addition, we discuss several challenges of automatic cataract classification and grading based on machine learning techniques and present possible solutions to these challenges for future research.

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Section: Feature Extractionmentioning

confidence: 99%

Machine Learning for Cataract Classification and Grading on Ophthalmic Imaging Modalities: A Survey

Zhang¹,

Fang²,

Hu³

et al. 2020

Preprint

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“…Adapun beberapa penelitian yang menggunakan SVM sebagai pengklasifikasi adalah sebagai berikut. Sistem klasifikasi katarak serius dan non serius menggunakan metode analisis tekstur statistik dan SVM dengan akurasi 86,6% [5], sistem deteksi katarak untuk kondisi normal, katarak dan post cataract menggunakan metoda Edge pixel Count dan SVM dengan akurasi 88,39% [6], sistem deteksi katarak untuk kondisi katarak insipien dan katarak lanjut menggunakan metode Principal Component Analysis (PCA) dan SVM dengan akurasi 89% [7]. Adapun sistem deteksi katarak yang berbasis K-NN diantaranya adalah sistem klasifikasi kondisi mata normal dan katarak menggunakan metode analisis tekstur statistik dan K-NN dengan akurasi 94,5% [8].…”

Section: Pendahuluanunclassified

Optimasi K-Nearest Neighbor Untuk Sistem Klasifikasi Kondisi Katarak

Fuadah

Magdalena²,

Palondongan³

et al. 2019

tektrika

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Deteksi dini kondisi katarak merupakan solusi untuk mengendalikan peningkatan jumlah kebutaan yangdisebabkan oleh katarak. Dalam penelitian ini, dilakukan optimasi metode Gray Level Co-Occurrence Matrix(GLCM) dan K-Nearest Neighbor (K-NN) untuk klasifikasi kondisi citra mata menjadi kondisi normal, katarakimatur dan katarak matur. Pada tahap ekstraksi ciri digunakan metode GLCM, parameter yang berpengaruhpada tahap ini adalah jarak antar piksel (d), sudut (q), dan fitur statistik. Pada tahap klasifikasi, fitur ciri daritahap ekstraksi ciri diklasifikasikan oleh K-NN, parameter yang diuji pada tahap ini adalah nilai k dan persamaanperhitungan jarak yang digunakan yaitu Euclidean, Minkowski, Chebychev dan City Block. Berdasarkan hasilpengujian diperoleh akurasi pengujian sebesar 93,33 % dengan menggunakan parameter yang paling optimalyaitu d= 2, q= 135, nilai k=5 dengan persamaan jarak Euclidean dan Minkowski.

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“…Principle Component Analysis (PCA) is applied on these features. Support Vector Machine algorithm (SVM) [15] is used for classification of fundus image. Their future work was intended to use other machine learning algorithms for better accuracy and efficiency.…”

Section: Related Workmentioning

confidence: 99%

Automatic Cataract Classification on Retinal Image using Support Vector Machine

Kumari¹,

Jagadev²

2017

IJARCSSE

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Abstract-Eye is a delicate organ of the body which provides organisms a vision. Eye is made up of sensory component such as lens, pupil, retina etc. One of the diseases which affect the human eye is cataract. Cataract occurs due to clouding of lens in the eye. Cataract is an eye disease which is responsible for vision loss and blindness. But earlier cataract detection system can provide a patient to know their condition timely and they can get the treatment accordingly. Using various image processing and classification technique one can detect and classify images. This paper points out different algorithm for detecting cataract in fundus images. This paper mainly involves mainly three steps specially preprocessing of the image, extraction of feature of preprocessed image and the last one is classification of image. In the very first step, image processing technique is applied for processing the image. We have used brightness preserving dynamic fuzzy histogram equalization method for contrast enhancement of image. In second step various feature of optical eye is extracted and the same feature are then used in classifier. For feature extraction statistical texture features such as mean, variance, energy, entropy and kurtosis of the eye is found. Support Vector Machine (SVM). SVM classification accuracy is 89%.

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New approach for objective cataract classification based on ultrasound techniques using multiclass SVM classifiers

Cited by 20 publications

References 28 publications

Machine Learning for Cataract Classification and Grading on Ophthalmic Imaging Modalities: A Survey

Machine Learning for Cataract Classification and Grading on Ophthalmic Imaging Modalities: A Survey

Optimasi K-Nearest Neighbor Untuk Sistem Klasifikasi Kondisi Katarak

Automatic Cataract Classification on Retinal Image using Support Vector Machine

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