A non-invasive methodology for the grade identification of astrocytoma using image processing and artificial intelligence techniques

Subashini, M. Monica; Sahoo, Sarat Kumar; Sunil, V. B.; Easwaran, Sudha

doi:10.1016/j.eswa.2015.08.036

Cited by 82 publications

(22 citation statements)

References 13 publications

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“…The utilization of Naïve Bayes has grown rapidly for image retrieval and classification [28][29]. Naïve Bayes surpass SVM in research on brain tumor recognition to identify two classes of brain tumor [30]. On the other hand, SVM successfully achieves higher classification accuracy in bruise and cultivar of apple detection in agriculture product [31].…”

Section: Support Vector Machine (Svm) Is a Well Know Classifier And Hmentioning

confidence: 99%

Palm oil fresh fruit bunch ripeness grading identification using color features

Sabri¹,

Ibrahim²,

Syahlan³

et al. 2018

J. Fundam and Appl Sci.

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Section: Support Vector Machine (Svm) Is a Well Know Classifier And Hmentioning

confidence: 99%

Palm oil fresh fruit bunch ripeness grading identification using color features

Sabri¹,

Ibrahim²,

Syahlan³

et al. 2018

J. Fundam and Appl Sci.

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show abstract

“…The CEC2014 benchmark suite consists of 30 test functions, which include three unimodal functions, 13 multimodal functions, six hybrid functions and eight composite functions. In particular, these hybrid functions (f17-f22) are very similar to real world problems, such as transportation networks [46], circuit theory [47], image processing [48], capacitated arc routing problems [49] and flexible job-shop scheduling problems [50]. The search range for each function is [−100, 100] DIM , where DIM is the dimension of the problem.…”

Section: Parameters and Benchmark Functionsmentioning

confidence: 99%

A New Differential Mutation Based Adaptive Harmony Search Algorithm for Global Optimization

Zhao

Hao

et al. 2020

Applied Sciences

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The canonical harmony search (HS) algorithm generates a new solution by using random adjustment. However, the beneficial effects of harmony memory are not well considered. In order to make full use of harmony memory to generate new solutions, this paper proposes a new adaptive harmony search algorithm (aHSDE) with a differential mutation, periodic learning and linear population size reduction strategy for global optimization. Differential mutation is used for pitch adjustment, which provides a promising direction guidance to adjust the bandwidth. To balance the diversity and convergence of harmony memory, a linear reducing strategy of harmony memory is proposed with iterations. Meanwhile, periodic learning is used to adaptively modify the pitch adjusting rate and the scaling factor to improve the adaptability of the algorithm. The effects and the cooperation of the proposed strategies and the key parameters are analyzed in detail. Experimental comparison among well-known HS variants and several state-of-the-art evolutionary algorithms on CEC 2014 benchmark indicates that the aHSDE has a very competitive performance.

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“…Recognition of essential features leads us to design an efficient system. With feature extraction, the time, data and memory get decreased [40]. The feature extraction stage is extremely important as the outcome is calculated based on these extracted features data [41].…”

Section: B Feature Extractionmentioning

confidence: 99%

Critical Analysis of Brain Magnetic Resonance Images Tumor Detection and Classification Techniques

Ullah¹,

Lee²,

An³

2020

IJACSA

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The image segmentation, tumor detection and extraction of tumor area from brain MR images are the main concern but time-consuming and tedious task performed by clinical experts or radiologist, while the accuracy relies on their experiences only. So, to overcome these limitations, the usage of computer-aided design (CAD) technology has become very important. Magnetic resonance imaging (MRI) and Computed Tomography (CT) are the two major imaging modalities that are used for brain tumor detection. In this paper, we have carried out a critical review of different image processing techniques of brain MR images and critically evaluate these different image processing techniques in tumor detection from brain MR images to identify the gaps and limitations of those techniques. Therefore, to obtain precise and better results, the gaps can be filled and limitations of various techniques can be improved. We have observed that most of the researchers have employed these stages such as Pre-processing, Feature extraction, Feature reduction, and Classification of MR images to find benign and malignant images. We have made an effort in this area to open new dimensions for the readers to explore the concerned field of research.

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A non-invasive methodology for the grade identification of astrocytoma using image processing and artificial intelligence techniques

Cited by 82 publications

References 13 publications

Palm oil fresh fruit bunch ripeness grading identification using color features

Palm oil fresh fruit bunch ripeness grading identification using color features

A New Differential Mutation Based Adaptive Harmony Search Algorithm for Global Optimization

Critical Analysis of Brain Magnetic Resonance Images Tumor Detection and Classification Techniques

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