Application of SVM based on genetic algorithm in classification of cataract fundus images

Qiao, Zhiqiang; Zhang, Qinyan; Dong, Yanyan; Yang, Ji-Jiang

doi:10.1109/ist.2017.8261541

Cited by 37 publications

(12 citation statements)

References 6 publications

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“…This encourages the precision of the original photos of the fund to Fig. 4: Flow chart of cataract classification [59] be retained as much as practicable. In order to create the best weight range between the features in the algorithm, the genetic algorithm is used to achieve the best classification result for the classifier.…”

Section: B Literature-based Disease Diagnosis Applicationsmentioning

confidence: 97%

“…Since the most critical role in the area of health care is the detection of illness. When an infection is detected early, several lives will be spared [52][53][54][55][56][57][58][59][60][61].…”

Section: Applications Of Support Vector Machine (Svm)mentioning

confidence: 99%

“…Regarding the application of diagnosing diseases, various works [52][53][54][55][56][57][58][59][60][61] have been implemented using machine learning techniques. Where, techniques of machine learning classification it is very useful in the medical field that provides a quick diagnosis of diseases and accurate, an approach for segmentation of MRI, CT and Ultrasound images was proposed by Asuntha et al [55] cancer cell identification is performed by analyzing the required characteristics for the two images extracted.…”

Section: B Literature-based Disease Diagnosis Applicationsmentioning

confidence: 99%

“…The findings revealed a precision of 99.2 percent in detecting normal-class CAD using the proposed algorithm. Furthermore, Qiao et al [59] propose a genetic algorithmbased SVM classifier for feature weighting and cataract detection. A cataract is one of the most serious illnesses that can cause blindness.…”

Section: B Literature-based Disease Diagnosis Applicationsmentioning

confidence: 99%

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Machine Learning Applications based on SVM Classification A Review

Abdullah¹,

Abdulazeez²

2021

QAJ

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Extending technologies and data development culminated in the need for quicker and more reliable processing of massive data sets. Machine Learning techniques are used excessively. This paper, therefore, attempts to deal with data processing, using a support vector machine (SVM) algorithm in different fields since it is a reliable, efficient classification method in the area of machine learning. Accordingly, many works have been explored in this paper to cover the use of SVM classifier. Classification based on SVM has been used in many fields like face recognition, diseases diagnostics, text recognition, sentiment analysis, plant disease identification and intrusion detection system for network security application. Based on this study, it can be concluded that SVM classifier has obtained high accuracy results in most of the applications, specifically, for face recognition and diseases identification applications.

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Section: B Literature-based Disease Diagnosis Applicationsmentioning

confidence: 97%

Section: Applications Of Support Vector Machine (Svm)mentioning

confidence: 99%

Section: B Literature-based Disease Diagnosis Applicationsmentioning

confidence: 99%

Section: B Literature-based Disease Diagnosis Applicationsmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning Applications based on SVM Classification A Review

Abdullah¹,

Abdulazeez²

2021

QAJ

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“…SVM models present an advantage in comparison with other methods, for example, partial least square-discriminant analysis, to model classification of nonlinear problems [39]. As a result of this advantage, the SVM can be applied in different research areas such as agricultural sciences [38,40], medicine [41,42], or Economics [43,44], inter alia.…”

Section: Introductionmentioning

confidence: 99%

Random Forest, Artificial Neural Network, and Support Vector Machine Models for Honey Classification

Martinez-Castillo

Astray

Mejuto

et al. 2019

eFood

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Different separated protein fractions by the electrophoretic method in polyacrylamide gel were used to classify two different types of honeys, Galician honeys and commercial honeys produced and packaged outside of Galicia. Random forest, artificial neural network, and support vector machine models were tested to differentiate Galician honeys and other commercial honeys produced and packaged outside of Galicia. The results obtained for the best random forest model allowed us to determine the origin of honeys with an accuracy of 95.2%. The random forest model, and the other developed models, could be improved with the inclusion of new data from different commercial honeys.

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