Automatic Cataract Classification based on Ultrasound Technique Using Machine Learning: A comparative Study

Caxinha, Miguel; Velte, Elena; Santos, Mário; Perdigão, Fernando; Amaro, João; Gomes, Marco; Santos, Jaime

doi:10.1016/j.phpro.2015.08.263

Cited by 26 publications

(12 citation statements)

References 9 publications

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“…Dengan 9-fold cross-validation, tingkat klasifikasi tertinggi 91,5% dan 90% dicapai menggunakan model CNN. Caxinha, et al, [5] klasifikasi katarak berdasarkan teknik ultrasound menggunakan klasifikasi Bayes, K-Nearest Neighbours (KNN), Fishes Linear Discriminant (FLD), dan Support Vector Machine (SVM) dengan hasil SVM menunjukkan kinerja tertinggi 90.62%.…”

Section: Pendahuluanunclassified

Klasifikasi Pengembalian Radar dari Ionosfer Menggunakan SVM, NaÃ¯ve Bayes dan Random Forest

Aryani¹,

Wijayanto²

2021

Komputika

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ABSTRAK â€“ Klasifikasi merupakan salah satu topik utama dalam data mining atau machine learning. Klasifikasi adalah suatu pengelompokan data dimana data yang digunakan tersebut mempunyai kelas label atau target. Klasifikasi digunakan untuk mengambil data dan ditempatkan kedalam kelompok tertentu. Studi tentang ionosfer penting untuk penelitian di berbagai domain, khususnya dalam sistem komunikasi. Dalam penelitian ionosfer, perlu dilakukan klasifikasi radar yang berguna dan tidak berguna dari ionosfer. Pada makalah ini, akan dilakukan klasifikasi terhadap data inosphere yang diambil dari UCI machine learning repository. Klasifikasi dilakukan dengan menggunakan tiga metode klasifikasi, yakni SVM ( Support Vector Machine ) , NaÃ¯ve Bayes, dan Random Forest. Hasil dari percobaan ini bisa menunjukkan prediksi dari setiap percobaan dengan tingkat akurasi dan prediksi yang berbeda-beda di setiap metode yang digunakan. Hasil akurasi, presisi, dan recall terbaik didapatkan pada metode Random Forest dengan rasio data latih dan data uji sebesar 85% didapat akurasi dari data uji sebesar 90,57% dengan presisi sebesar 94,12%. Kata Kunci â€“ Ionosfer; Klasifikasi; SVM; NaÃ¯ve Bayes; Random Forest.

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Section: Pendahuluanunclassified

Klasifikasi Pengembalian Radar dari Ionosfer Menggunakan SVM, NaÃ¯ve Bayes dan Random Forest

Aryani¹,

Wijayanto²

2021

Komputika

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“…Fuadah et al [33] used the KNN to detect cataract on digital camera images and achieved 97.2% accuracy. Literature [12] also uses KNN for cataract classification on Ultrasonic images, which collected from the animal model.…”

Section: Classificationandgradingmentioning

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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“…They found that the computation cost decreased a lot on the basis of new features obtained after PCA transformation with the original result. M. Caixinha et al [13] developed a method which compared four classification techniques. They presented a method based on ultrasound technique using machine learning algorithm for Automatic Cataract Classification.…”

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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Automatic Cataract Classification based on Ultrasound Technique Using Machine Learning: A comparative Study

Cited by 26 publications

References 9 publications

Klasifikasi Pengembalian Radar dari Ionosfer Menggunakan SVM, NaÃ¯ve Bayes dan Random Forest

Klasifikasi Pengembalian Radar dari Ionosfer Menggunakan SVM, NaÃ¯ve Bayes dan Random Forest

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

Automatic Cataract Classification on Retinal Image using Support Vector Machine

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