Application of gray level run length matrices features extraction for diabetic retinopathy detection based on artificial neural network

2020 ◽  
Author(s):  
Bestia Kumala Wardani ◽  
Nathania Earlene Belinda ◽  
Riries Rulaningtyas ◽  
Endah Purwanti
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Diabetic Retinopathy ◽  
Features Extraction ◽  
Gray Level ◽  
Run Length ◽  
Artificial Neural

scholarly journals Tri-Concomitant Local Feature Learning for Diabetic Retinopathy Classification

2019 ◽  
Vol 9 (2) ◽  
pp. 143-147
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Diabetic Retinopathy ◽  
Feature Learning ◽  
Intensity Variation ◽  
Structural Element ◽  
Directional Information ◽  
Artificial Neural ◽  
A New Technique ◽  
Rule Based Approach

In this paper, we have proposed a new technique entitled as Transformed Directional Tri Concomitant Triplet Patterns with Artificial Neural Network is proposed for Diabetic Retinopathy Classification. TdtCTp consist of three stages to obtain detail directional information about pixel progression. In first stage, structural rule based approach is proposed to extract directional information in various direction. Further, in second stage, microscopic information and correlation between each sub-structural element are extracted by using concomitant conditions. Finally, minute directional intensity variation information and correlation between the sub-structural elements are extracted by integrating first two stages. After feature extraction, the extracted feature is used as input to the artificial neural network. To the best of our knowledge, this is the first learning based approach for diabetic retinopathy classification. Effectiveness of the proposed method is evaluated in terms of average precision and compared with existing state-of-the-art methods. The experimental analysis shows that the proposed method is achieved significant performance compared to other methods.

scholarly journals Design of an OCR System and its Hardware Implementation

2021 ◽  
Vol 9 (12) ◽  
pp. 159-174
Author(s):  
Gulfeshan Parween
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Character Recognition ◽  
Hardware Implementation ◽  
Gray Level ◽  
Document Images ◽  
English Alphabet ◽  
Barcode Recognition ◽  
Printed Text ◽  
Artificial Neural

Abstract: In this paper, we present a scheme to develop to complete OCR system for printed text English Alphabet of Uppercase of different font and of different sizes so that we can use this system in Banking, Corporate, Legal industry and so on. OCR system consists of different modules like preprocessing, segmentation, feature extraction and recognition. In preprocessing step it is expected to include image gray level conversion, binary conversion etc. After finding out the feature of the segmented characters artificial neural network and can be used for Character Recognition purpose. Efforts have been made to improve the performance of character recognition using artificial neural network techniques. The proposed OCR system is capable of accepting printed document images from a file and implemented using MATLAB R2014a version. Key words: OCR, Printed text, Barcode recognition

scholarly journals Artificial Neural Network dalam Menentukan Grading Histopatologi Kanker Payudara

2017 ◽  
Vol 19 (2) ◽  
pp. 176
Author(s):  
Agoes Santika Hyperastuty
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Artificial Neural Network ◽  
Learning Rate ◽  
Gray Level ◽  
Artificial Neural ◽  
Hidden Layer

Abstrak Kanker payudara adalah jenis tumor ganas utama yang diamati pada wanita dan pengobatan yang efektif tergantung pada diagnosis awalnya. Standar emas pemeriksaan kanker payudara adalah pemeriksaan histopatologis sel kanker. Penentuan kadar pada kanker payudara ditentukan oleh tiga faktor: pleomorfik, pembentukan tubular dan mitosis sel. Dalam tulisan ini mengacu pada formasi pleumorfic dan tubular oleh gambar histopatologi sel payudara. Sistem yang diusulkan terdiri dari empat langkah utama: preprocessing, segmentation, ekstrasi fitur dan identifikasi. Pada proses segmentasi  menggunakan metode K-Means Clustering yaitu mengelompokkan data menurut kesamaan warna dan bentuk. Hasil dari K-Means tersebut berupa matrik.  Ekstraksi fitur menggunakan Gray level Cooccurence Matrix (GLCM) yaitu  tingkat keabuan masing-masing citra yang dilihat dari  4 fiturnya adalah kontras, energi, entropi dan homogenitas. Langkah terakhir adalah identifikasi menggunakan Backpropagation. Beberapa parameter penting akan divariasikan dalam proses ini seperti learning rate dan jumlah node pada hidden layer. Hasil penelitian menunjukkan bahwa fitur ekstraksi dalam 4 fitur adalah akurasi terbaik berdasarkan kelas 81,1% dan khususnya ketepatannya adalah 80%.Kata kunci—Histopatologic breast cancer, kmeans, GLCM, Backpropagation

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