Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy

Welikala, Roshan Alex; Fraz, Muhammad Moazam; Dehmeshki, Jamshid; Hoppe, Andreas; Tah, Vikas; Mann, Samantha; Williamson, Tom H.; Barman, Sarah

doi:10.1016/j.compmedimag.2015.03.003

Cited by 147 publications

(68 citation statements)

References 62 publications

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“…58 Moreover, computers are suitable to issues involving the derivation of quantitative information from images because of their capacity to process data in fast and efficient manner with a high degree of reproducibility. [59][60][61] In this paper, we proposed a hierarchical method based on combination of matching based approach and CNNs to detect all MAs from color fundus retinal image. Although the results of MAs detection without vessel masking was promising, to avoid more false positive and screen more patients which they dont need this is better to segment and mask retinal vessel as a preprocessed task before final MAs detection.…”

Section: Resultsmentioning

confidence: 99%

The efficacy of microaneurysms detection with and without vessel segmentation in color retinal images

Tavakoli

Nazar

Mehdizadeh

2020

Medical Imaging 2020: Computer-Aided Diagnosis

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Computer-Aided Diagnosis systems are required to extract suitable information about retina and its changes. In particular, identifying objects of interest such as lesions and anatomical structures from the retinal images is a challenging and iterative process that is doable by image processing approaches. Microaneurysm (MAs) are one set of these changes that caused by diabetic retinopathy (DR). In fact, MAs detection is the main step for identification of DR in the retinal images analysis. The objective of this study is to apply an automated method for detection of MAs and compare the results of detection with and without vessel segmentation and masking either in the normal or abnormal image. The steps for the detection and segmentation are as follows. At the first step, we did preprocessing, by using top-hat transformation. Our main processing was included applying Radon transform, to segment the vessels and masking them. At last, we did MAs detection step using combination of Laplacian-of-Gaussian and Convolutional Neural Networks. To evaluate the accuracy of our proposed method, we compare the output of our proposed method with the ground truth that collected by ophthalmologists. With vessel segmentation, our algorithm found sensitivity of more than 85% in detection of MAs with 11 false positive rate per image for 100 color images in a local retinal database and 20 images of a public dataset (DRIVE). Also without vessel segmentation, our automated algorithm finds sensitivity of about 90% in detection of MAs with 73 false positives per image for all 120 images of two databases. In conclusion, with vessel segmentation we have acceptable sensitivity and specificity, as a necessary step in some diagnostic algorithm for retinal pathology.

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Section: Resultsmentioning

confidence: 99%

The efficacy of microaneurysms detection with and without vessel segmentation in color retinal images

Tavakoli

Nazar

Mehdizadeh

2020

Medical Imaging 2020: Computer-Aided Diagnosis

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“…If parents have better fitness, their offspring will be better than parents and have a better chance of surviving. This process keeps on iterating, and at the end, a generation with the fittest individuals will be found [10].…”

Section: Genetic Alghorithmmentioning

confidence: 99%

“…while w is the weight sought for each type of wave where the sum of w must be 1 like equation 3. In the process of the genetic algorithm we will find the best w weights to be multiplied by each of the types of waves that have been taken on average to determine that weight [10], [11]. An individual is characterized by a set of parameters (variables) known as Genes.…”

Section: Genetic Alghorithmmentioning

confidence: 99%

Brain Signal Classification using Genetic Algorithm for Right-Left Motion Pattern

Rahmad¹,

Ariyanto²,

Yunianto³

2018

ijacsa

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Brain signals or EEG are non-stationary signals and are difficult to analyze visually. The brain signal has five waves alpha, beta, delta, gamma, and theta. The five waves have their frequency to describe the level of attention, alertness, character and external stimuli. The five waves can be used to analyze stimulation patterns when turning left and right. Giving weight to the five brain waves utilizes genetic algorithms to get one signal. Genetic algorithms can be used to find the best signal for classification. In this paper, the EEG signal will be classified to determine the right or left movement pattern. After combining the five brain waves with genetic algorithms is then classified using the Logistic Regression, Linear Discriminant Analysis, K-Neighbors Classifier, Decision Tree, Naïve Bayes Gaussian, and Support Vector Machine. From the six methods above that have the highest accuracy is 56% and SVM is a method that has better accuracy than others on this problem.

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“…Crossover is an operator present in the reproductive process. The perform of crossover operator is the cross-breeding of the two parents to produce a new individual [12]. The number of new individuals is produced from crossover operator is obtained by multiplying the parameter crossover rate (Cr) and the number of population size (popSize).…”

Section: Calculate the Fitness Functionmentioning

confidence: 99%

Cost Optimization of Multi-Level Multi-Product Distribution Using An Adaptive Genetic Algorithm

Sarwani

Mahmudy

Naba

2017

JITeCS

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Abstract. Distribution is the challenging and interesting problem to be solved. Distribution problems have many facets to be resolved because it is too complex problems such as limited multi-level with one product, one-level and multi-product even desirable in terms of cost also has several different versions. In this study is proposed using an adaptive genetic algorithm that proved able to acquire efficient and promising result than the classical genetic algorithm. As the study and the extension of the previous study, this study applies an adaptive genetic algorithm considering the problems of multi-level distribution and combination of various products. This study considers also the fixed cost and variable cost for each product for each level distributor. By using the adaptive genetic algorithm, the complexity of multi-level and multi-product distribution problems can be solved. Based on the cost, the adaptive genetic algorithm produces promising result compared to the existing algorithm

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Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy

Cited by 147 publications

References 62 publications

The efficacy of microaneurysms detection with and without vessel segmentation in color retinal images

The efficacy of microaneurysms detection with and without vessel segmentation in color retinal images

Brain Signal Classification using Genetic Algorithm for Right-Left Motion Pattern

Cost Optimization of Multi-Level Multi-Product Distribution Using An Adaptive Genetic Algorithm

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