Hybrid Genetic Algorithm for Medical Image Feature Extraction and Selection

Nagarajan, G.; Minu, R. I.; Muthukumar, B.; Vedanarayanan, V.; Sundarsingh, Shirly

doi:10.1016/j.procs.2016.05.192

Cited by 42 publications

(11 citation statements)

References 5 publications

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“…The number of iteration steps and the size of population are 50 and 30, respectively. Genetic feature selection has been applied in many fields, such as image classification [43], text categorization [44], image feature extraction [45] and signal processing [46].…”

Section: Methodsmentioning

confidence: 99%

Prediction of postoperative complications of pediatric cataract patients using data mining

et al. 2019

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BackgroundThe common treatment for pediatric cataracts is to replace the cloudy lens with an artificial one. However, patients may suffer complications (severe lens proliferation into the visual axis and abnormal high intraocular pressure; SLPVA and AHIP) within 1 year after surgery and factors causing these complications are unknown.MethodsApriori algorithm is employed to find association rules related to complications. We use random forest (RF) and Naïve Bayesian (NB) to predict the complications with datasets preprocessed by SMOTE (synthetic minority oversampling technique). Genetic feature selection is exploited to find real features related to complications.ResultsAverage classification accuracies in three binary classification problems are over 75%. Second, the relationship between the classification performance and the number of random forest tree is studied. Results show except for gender and age at surgery (AS); other attributes are related to complications. Except for the secondary IOL placement, operation mode, AS and area of cataracts; other attributes are related to SLPVA. Except for the gender, operation mode, and laterality; other attributes are related to the AHIP. Next, the association rules related to the complications are mined out. Then additional 50 data were used to test the performance of RF and NB, both of then obtained the accuracies of over 65% for three classification problems. Finally, we developed a webserver to assist doctors.ConclusionsThe postoperative complications of pediatric cataracts patients can be predicted. Then the factors related to the complications are found. Finally, the association rules that is about the complications can provide reference to doctors.Electronic supplementary materialThe online version of this article (10.1186/s12967-018-1758-2) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Prediction of postoperative complications of pediatric cataract patients using data mining

et al. 2019

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“…Further, the image is segmented [10] by active contour without edge for making difference between defects and non-defects area in region wise. Each region [11] is partitioned based on certain texture characteristic and proper-ties in homogeneously. Using Homogeneity, the measures of closeness of the distribution could be obtained to determine whether the image is textured or non-textured.…”

Section: Fig 2 Intermittent Leather With Defect •mentioning

confidence: 99%

“…For data analysis, statistical software SPSS V20 has been used.To understand the relationship between data of basecolor and shareofcuttingvalue as shown in equation (11), correlation is applied. There is a strong positive correlation of 0.985 between basecolor and shareofcuttingvalue.…”

Section: Glcm Features Of Non-defect Intermittent Leathermentioning

confidence: 99%

Surface Defect Identification and Grouping of Intermittent Leather Images using Linear Discriminant Model

Vasagam¹,

Sornam²

2019

IJITEE

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Scrutiny of intermittent leather is accepted through visual analysis on the natural material by an experienced individual based on many parameters which includes surface defects as a parameter. Such results comprising of base color, other than base color, share of regions, share of cutting area, share of cutting value, position wise length and position wise breadth will determine the value of the leather and surprisingly the result will vary form one experienced person to another. Hence, a new method for grouping of intermittent leather is proposed for a better or suitable decision making. Feature extraction technique, Grey Level Co-occurrence Matrix (GLCM) has been implemented to understand the features of color and area by extracting the texture features like Entropy, Energy, Contrast, Variance, Mean, Dissimilarity, Correlation and Homogeneity. A total of 428 intermittent leather imagesare used to understand the classification. The classifiers, Linear Discriminant Analysis (LDA) model and Support Vector Machine (SVM) are used to find out the accuracy. Further, linear discriminant model confirms 92% of accuracy over the support vector machine which is confirms 89.65% of accuracy. The proposed LDA model clearly shows that the approach is successful in classifying the variations among the defects and non-defects in intermittent leather images.

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“…It efficiently selects features from high dimensional data sets where exhaustive search is not feasible. Examples of IoT use-cases that have utilized genetic algorithm include intrusion detection [118], medical image feature extraction and selection [119], pattern recognition [120], building energy optimization [121], gait analysis [122], etc. Table 4.4 presents the purpose of dimensionality reduction techniques in IoT use-cases mentioned in this sub-section.…”

mentioning

confidence: 99%

Data Analytics in the Internet of Things: A Survey

Saleem

Chishti

2019

SCPE

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The plethora of sensors deployed in Internet of Things (IoT) environments generate unprecedented volumes of data, thereby creating a data deluge. Data collected from these sensors can be used to comprehend, examine and control intricate environments around us, facilitating greater intelligence, smarter decision-making, and better performance. The key challenge here is how to mine out proﬁcient information from such immense data. Copious solutions have been put forth to obtain valuable inferences and insights, however, these solutions are still in their developing stages. Moreover, conventional procedures do not address the surging analytical demands of IoT systems. Motivated to resolve this concern, this work investigates the key enablers for performing desired data analytics in IoT applications. A comprehensive survey on the identiﬁed key enablers including their role in IoT data analytics, use cases in which they have been applied and the corresponding IoT applications for the use cases is presented. Furthermore, open research challenges and future research opportunities are also discussed. This article can be used as a basis to foster advanced research in the arena of IoT data analytics.

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Hybrid Genetic Algorithm for Medical Image Feature Extraction and Selection

Cited by 42 publications

References 5 publications

Prediction of postoperative complications of pediatric cataract patients using data mining

Prediction of postoperative complications of pediatric cataract patients using data mining

Surface Defect Identification and Grouping of Intermittent Leather Images using Linear Discriminant Model

Data Analytics in the Internet of Things: A Survey

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