INTEGRATING FUZZY C-MEANS AND MAHALANOBIS METRIC CLASSIFICATION FOR EXUDATE DETECTION IN COLOR FUNDUS IMAGING

2015 ◽  
Vol 15 (05) ◽  
pp. 1550085 ◽  
Author(s):  
MADHURI TASGAONKAR ◽  
MADHURI KHAMBETE
Keyword(s):  
Diabetic Retinopathy ◽  
Early Detection ◽  
Ground Truth ◽  
Vital Role ◽  
Detection Accuracy ◽  
Fuzzy C Means ◽  
Fundus Imaging ◽  
Imaging Detection ◽  
Retinal Structure

Diabetes affects retinal structure of a diabetic patient by generating various lesions. Early detection of these lesions can avoid the loss of vision. Automation of detection process can be made easily feasible to masses by the use of fundus imaging. Detection of exudates is significant in diabetic retinopathy (DR) as they are earlier signs and can cause blindness. Finding the exact location as well as correct number of exudates play vital role in the overall treatment of a patient. This paper presents an algorithm for automatic detection of exudates for DR. The algorithm combines the advantages of supervised and unsupervised techniques. It uses fuzzy-C means (FCM) segmentation on coarse level and mahalanobis metric for finer classification of segmented pixels. Mahalanobis criterion gives significance to most relevant features and thus proves a better classifier. The results are validated using DIARETDB0 and DIARETDB1 databases and the ground truth provided with it. This evaluation provided 95.77% detection accuracy.

scholarly journals Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

Plants ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1302 ◽  
Author(s):  
Reem Ibrahim Hasan ◽  
Suhaila Mohd Yusuf ◽  
Laith Alzubaidi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Early Detection ◽  
Feature Fusion ◽  
State Of The Art ◽  
Vital Role ◽  
Considerable Improvement ◽  
Plant Diseases ◽  
Recent Emergence

Deep learning (DL) represents the golden era in the machine learning (ML) domain, and it has gradually become the leading approach in many fields. It is currently playing a vital role in the early detection and classification of plant diseases. The use of ML techniques in this field is viewed as having brought considerable improvement in cultivation productivity sectors, particularly with the recent emergence of DL, which seems to have increased accuracy levels. Recently, many DL architectures have been implemented accompanying visualisation techniques that are essential for determining symptoms and classifying plant diseases. This review investigates and analyses the most recent methods, developed over three years leading up to 2020, for training, augmentation, feature fusion and extraction, recognising and counting crops, and detecting plant diseases, including how these methods can be harnessed to feed deep classifiers and their effects on classifier accuracy.

scholarly journals Classification of Surface Water Using Machine Learning Methods from Landsat Data in Nepal

Proceedings ◽  
2018 ◽  
Vol 4 (1) ◽  
pp. 43 ◽  
Author(s):  
Tri Dev Acharya ◽  
Anoj Subedi ◽  
He Huang ◽  
Dong Ha Lee
Keyword(s):  
Machine Learning ◽  
Surface Water ◽  
Ground Truth ◽  
Vital Role ◽  
Google Earth ◽  
Landsat 8 ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Water Classification

With over 6000 rivers and 5358 lakes, surface water is one of the most important resources in Nepal. However, the quantity and quality of Nepal’s rivers and lakes are decreasing due to human activities and climate change. Therefore, the monitoring and estimation of surface water is an essential task. In Nepal, surface water has different characteristics such as varying temperature, turbidity, depth, and vegetation cover, for which remote sensing technology plays a vital role. Single or multiple water index methods have been applied in the classification of surface water with satisfactory results. In recent years, machine learning methods with training datasets, have been outperforming different traditional methods. In this study, we tried to use satellite images from Landsat 8 to classify surface water in Nepal. Input of Landsat bands and ground truth from high resolution images available form Google Earth is used, and their performance is evaluated based on overall accuracy. The study will be will helpful to select optimum machine learning methods for surface water classification and therefore, monitoring and management of surface water in Nepal.

Multispectral fundus imaging for early detection of diabetic retinopathy

1999 ◽  
Author(s):  
James M. Beach ◽  
James S. Tiedeman ◽  
Mark F. Hopkins ◽  
Yashvinder S. Sabharwal
Keyword(s):  
Diabetic Retinopathy ◽  
Early Detection ◽  
Fundus Imaging

scholarly journals Deep Learning based Method for Multi-class Classification of Diabetic Retinopathy

2021 ◽  
Vol 2 (2) ◽  
pp. 1-5
Author(s):  
Komal Damodara ◽  
Keyword(s):  
Diabetes Mellitus ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Blood Sugar Level ◽  
Retinal Disease ◽  
Fundus Imaging ◽  
Retinal Blood Vessels ◽  
The World ◽  
Multi Class Classification

Diabetes mellitus is a form of diabetes with secondary microvascular complication leading to renal dysfunction and retinal loss also termed as diabetic retinopathy. Retinopathy is grave form of retinal disease. It is the leading cause of blindness in the world. Blockage of tiny minute retinal blood vessels due to the high blood sugar level is the reason why retinopathy leads to blindness or loss of vision. This study serves the purpose of deep learning-based diagnosis of Diabetic retinopathy using the fundus imaging of the eye. In this study architectures such as VGG 16 and VGG 19 are deployed in order to classify the images into 5 categories. The performance of the two models were compared. The highest accuracy is 77.67% when using the VGG 16 pre-trained model.

scholarly journals Optimasi Fuzzy C-Means dan K-Means Menggunakan Algoritma Genetika untuk Pengklasteran Dataset Diabetic Retinopathy

2020 ◽  
Vol 7 (5) ◽  
pp. 993
Author(s):  
Muhammad Ezar Al Rivan ◽  
Steven Steven ◽  
William Tanzil
Keyword(s):  
Genetic Algorithm ◽  
Diabetic Retinopathy ◽  
Early Detection ◽  
Diabetic Complication ◽  
Algorithm Optimization ◽  
Genetic Algorithm Optimization ◽  
Fuzzy C Means ◽  
Before And After ◽  
Cluster Distance

<p class="Abstrak"><em>Diabetic Retinopathy</em> adalah komplikasi dari diabetes yang mengakibatkan gangguan pada retina mata. Gangguan tersebut dapat diketahui dengan deteksi awal melalui data yang diekstraksi dari citra mata. Deteksi awal dapat dilakukan dengan menggunakan metode <em>clustering</em>. Metode yang digunakan yaitu <em>Fuzzy C-Means</em> dan <em>K-Means</em>. <em>Fuzzy C-Means</em> dan <em>K-Means</em> memiliki kelemahan dari jumlah iterasi yang besar. Jumlah iterasi pada <em>Fuzzy C-Means</em> dan <em>K-Means</em> dapat dioptimasi dengan menggunakan Algoritma Genetika. Optimasi dilakukan dengan cara mengganti bagian pada <em>Fuzzy C-Means</em> dan <em>K-Means</em> pada saat menentukan pusat <em>cluster</em>. Dataset yang digunakan pada penelitian adalah dataset <em>Diabetic Retinopathy</em>. Hasil optimasi sebelum dan sesudah  hybrid Algoritma Genetika pada <em>Fuzzy C-Means</em> terlihat pada nilai rata-rata iterasi dari 17,1 menjadi 6,65 terjadi penurunan sebesar 61,11% dan pada <em>K-Means</em> terlihat pada nilai rata-rata iterasi dari 10,85 menjadi 7,35 terjadi penurunan sebesar 32,25%. Berdasarkan hasil perbandingan nilai rata-rata iterasi Algoritma Genetika–<em>Fuzzy C-Means</em> dan Algoritma Genetika-K-Means maka dapat disimpulkan bahwa Algoritma Genetika-<em>Fuzzy C-Means</em> memiliki jumlah iterasi yang lebih baik dibanding Algoritma Genetika-<em>K-Means</em>. Algoritma Genetika-<em>Fuzzy C-Means</em> juga memiliki <em>inter cluster distance </em>yang paling kecil dan <em>intra cluster distance </em>yang paling besar.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Diabetic Retinopathy is diabetic complication that cause retina disorder. Retina disorder can be known from data extracted from eye image. Early detection conduct using clustering. These methods are Fuzzy C-Means and K-Means. These methods have large number of iteration as weakness. Number of iteration can be optimized using genetic algorithm. Optimization conducted by replace a part from Fuzzy C-Means dan K-Means that use to generate early centroid. The dataset used in the study is a dataset of diabetic retinopathy. The optimization results before and after hybrid GeneticAlgorithm on Fuzzy C-Means are the average iteration values decreased from 17.1 to 6.65, decreasing 61,11% and in K-Means are the average iteration values decreased from 10.85 to 7.35 decreasing 32,25%. Based on the comparison of Genetic Algorithm  Fuzzy C-Means and Genetic Algorithm K-Means iterations, it can be concluded that Genetic Algorithm Fuzzy C-Means has a better number of iteration than Genetic Algorithm K-Means. Genetic Algorithm-Fuzzy-C-Means has smallest inter cluster distance and biggest intra cluster distance.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>

scholarly journals Lung Cancer Image- Feature Extraction and Classification using GLCM and SVM Classifier

2019 ◽  
Vol 8 (11) ◽  
pp. 2211-2215 ◽  
Keyword(s):  
Lung Cancer ◽  
Early Detection ◽  
Median Filter ◽  
Image Feature ◽  
World Health ◽  
Normal Lung ◽  
Svm Classifier ◽  
Early Detection Of Disease ◽  
Fuzzy C Means

Lung cancer is the second most causing cancer when compared to all the other cancers. According to WHO (World Health Organization) lung cancer contributes about 14 per cent among all the cancers. Therefore, early detection and treatment is very much required. Now-a- days, image processing techniques are playing a major role in early detection of disease which is very helpful in further treatment stages. These techniques help in detecting the abnormality of the tissues-tumor in target cancer images. In this research, the proposed methodology is majorly carried out in five phases. In phase one lung cancer and non-lung cancer, images are collected from the lung cancer database. In phase two preprocessing is done by using the Median filter. Median filter is chosen as it preserves the edges i.e, sharp features are preserved. In Phase three, segmentation of the target image is done using Fuzzy C Means. Fuzzy C Means Clustering is chosen as it gives better performance than K-means Clustering. In phase four, the features are extracted using GLCM (Gray Level Co-occurrence Matrix). GLCM have high discrimination accuracy and less computational speed. In phase five, these extracted features are given to SVM classifier for classification of lung cancer from normal lung. The SVM classier achieved accuracy of 96.7% for detecting and classification of lung cancer.

Sign in / Sign up

Export Citation Format

Share Document