An automated image processing method for classification of diabetic retinopathy stages from conjunctival microvasculature images

2017 ◽  
Author(s):  
Maziyar M. Khansari ◽  
William O’Neill ◽  
Richard Penn ◽  
Norman P. Blair ◽  
Felix Chau ◽  
...  
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Processing Method ◽  
Image Processing Method ◽  
Automated Image Processing

Automated image processing method to quantify rotating detonation wave behavior

2019 ◽  
Vol 90 (6) ◽  
pp. 065106 ◽  
Author(s):  
J. W. Bennewitz ◽  
B. R. Bigler ◽  
S. A. Schumaker ◽  
W. A. Hargus
Keyword(s):  
Image Processing ◽  
Detonation Wave ◽  
Processing Method ◽  
Image Processing Method ◽  
Automated Image Processing ◽  
Rotating Detonation

scholarly journals Quantitative analysis of chip segmentation in machining using an automated image processing method

Procedia CIRP ◽  
2019 ◽  
Vol 82 ◽  
pp. 314-319
Author(s):  
Andrew Hrechuk ◽  
Volodymyr Bushlya ◽  
Rachid M’Saoubi ◽  
Jan-Eric Ståhl
Keyword(s):  
Image Processing ◽  
Quantitative Analysis ◽  
Processing Method ◽  
Image Processing Method ◽  
Automated Image Processing

scholarly journals An automated image processing method to quantify collagen fibre organization within cutaneous scar tissue

2014 ◽  
Vol 24 (1) ◽  
pp. 78-80 ◽  
Author(s):  
Kyle P. Quinn ◽  
Alexander Golberg ◽  
G. Felix Broelsch ◽  
Saiqa Khan ◽  
Martin Villiger ◽  
...  
Keyword(s):  
Image Processing ◽  
Collagen Fibre ◽  
Scar Tissue ◽  
Processing Method ◽  
Image Processing Method ◽  
Automated Image Processing

scholarly journals Analisis Perbandingan KNN dengan SVM untuk Klasifikasi Penyakit Diabetes Retinopati berdasarkan Citra Eksudat dan Mikroaneurisma

2015 ◽  
Vol 3 (1) ◽  
pp. 75
Author(s):  
SUCI AULIA ◽  
SUGONDO HADIYOSO ◽  
DADAN NUR RAMADAN
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Diabetic Retinopathy ◽  
Test Data ◽  
Processing Method ◽  
Keywords Diabetic Retinopathy ◽  
Wavelet Method ◽  
Knn Classification ◽  
Normal Class

ABSTRAKPenelitian mengenai pengklasifikasian tingkat keparahan penyakit Diabetes Retinopati berbasis image processing masih hangat dibicarakan, citra yang biasa digunakan untuk mendeteksi jenis penyakit ini adalah citra optik disk, mikroaneurisma, eksudat, dan hemorrhages yang berasal dari citra fundus. Pada penelitian ini telah dilakukan perbandingan algoritma SVM dengan KNN untuk klasifikasi penyakit diabetes retinopati (mild, moderate, severe) berdasarkan citra eksudat dan microaneurisma. Untuk proses ekstraksi ciri digunakan metode wavelet  pada masing-masing kedua metode tersebut. Pada penelitian ini digunakan 160 data uji, masing-masing 40 citra untuk kelas normal, kelas mild, kelas moderate, kelas saviere. Tingkat akurasi yang diperoleh dengan menggunakan metode KNN lebih tinggi dibandingkan SVM, yaitu 65 % dan 62%. Klasifikasi dengan algoritma KNN diperoleh hasil terbaik dengan parameter K=9 cityblock. Sedangkan klasifikasi dengan metode SVM diperoleh hasil terbaik dengan parameter One Agains All.Kata kunci: Diabetic Retinopathy, KNN , SVM, Wavelet. ABSTRACT Research based on severity classification of the disease diabetic retinopathy by using image processing method is still hotly debated, the image is used to detect the type of this disease is an optical image of the disk, microaneurysm, exudates, and bleeding of the image of the fundus. This study was performed to compare SVM method with KNN method for classification of diabetic retinopathy disease (mild, moderate, severe) based on exudate and microaneurysm image. For feature extraction uses wavelet method, and each of the two methods. This study made use of 160 test data, each of 40 images for normal class, mild class, moderate class, severe class. The accuracy obtained by KNN higher than SVM, with 65% and 62%. KNN classification method achieved the best results with the parameters K = 9, cityblock. While the classification with SVM method obtained the best results with parameters One agains all .Keywords: Diabetic Retinopathy, KNN, SVM, Wavelet.

scholarly journals Classification of rat behavior with an image-processing method and a neural network

2000 ◽  
Vol 32 (1) ◽  
pp. 63-71 ◽  
Author(s):  
J. B. I. Rousseau ◽  
P. B. A. Van Lochem ◽  
W. H. Gispen ◽  
B. M. Spruijt
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Processing Method ◽  
Image Processing Method
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