Iris tissue recognition based on GLDM feature extraction and hybrid MLPNN-ICA classifier

Ahmadi, Neda; Akbarizadeh, Gholamreza

doi:10.1007/s00521-018-3754-0

Cited by 37 publications

(15 citation statements)

References 26 publications

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“…The NGTDM quantifies the difference between adjacent gray-level values within a specified distance through a matrix [21]. The GLDM quantifies the dependence of gray-levels in an area through a matrix [22]. The dependence of the gray-level is defined as the number of pixels connected within a particular distance and is dependent on the center pixel.…”

Section: Radiomic Feature Extractionmentioning

confidence: 99%

Machine Learning Models for Sarcopenia Identification Based on Radiomic Features of Muscles in Computed Tomography

Kim

2021

IJERPH

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The diagnosis of sarcopenia requires accurate muscle quantification. As an alternative to manual muscle mass measurement through computed tomography (CT), artificial intelligence can be leveraged for the automation of these measurements. Although generally difficult to identify with the naked eye, the radiomic features in CT images are informative. In this study, the radiomic features were extracted from L3 CT images of the entire muscle area and partial areas of the erector spinae collected from non-small cell lung carcinoma (NSCLC) patients. The first-order statistics and gray-level co-occurrence, gray-level size zone, gray-level run length, neighboring gray-tone difference, and gray-level dependence matrices were the radiomic features analyzed. The identification performances of the following machine learning models were evaluated: logistic regression, support vector machine (SVM), random forest, and extreme gradient boosting (XGB). Sex, coarseness, skewness, and cluster prominence were selected as the relevant features effectively identifying sarcopenia. The XGB model demonstrated the best performance for the entire muscle, whereas the SVM was the worst-performing model. Overall, the models demonstrated improved performance for the entire muscle compared to the erector spinae. Although further validation is required, the radiomic features presented here could become reliable indicators for quantifying the phenomena observed in the muscles of NSCLC patients, thus facilitating the diagnosis of sarcopenia.

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Section: Radiomic Feature Extractionmentioning

confidence: 99%

Machine Learning Models for Sarcopenia Identification Based on Radiomic Features of Muscles in Computed Tomography

Kim

2021

IJERPH

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“…GLRLM helps in obtaining higher-order statistical features consisting of a set of continuous pixels having similar gray levels [63]. GLDM extracts the features by computing a gray-level absolute difference method between two pixels separated by specific displacement [64]. Histogram-oriented gradient extracts feature by focusing on the structure of the image and uses the feature descriptor for counting the occurrence of gradient orientation in localized portion [65].…”

Section: Related Workmentioning

confidence: 99%

Hybrid bio-inspired algorithm and convolutional neural network for automatic lung tumor detection

Vijh¹,

Gaurav

Pandey

2020

Neural Comput & Applic

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In this paper, we have proposed a hybrid bio-inspired algorithm which takes the merits of whale optimization algorithm (WOA) and adaptive particle swarm optimization (APSO). The proposed algorithm is referred as the hybrid WOA_APSO algorithm. We utilize a convolutional neural network (CNN) for classification purposes. Extensive experiments are performed to evaluate the performance of the proposed model. Here, pre-processing and segmentation are performed on 120 lung CT images for obtaining the segmented tumored and non-tumored region nodule. The statistical, texture, geometrical and structural features are extracted from the processed image using different techniques. The optimized feature selection plays a crucial role in determining the accuracy of the classification algorithm. The novel variant of whale optimization algorithm and adaptive particle swarm optimization, hybrid bio-inspired WOA_APSO, is proposed for selecting optimized features. The feature selection grouping is applied by embedding linear discriminant analysis which helps in determining the reduced dimensions of subsets. Twofold performance comparisons are done. First, we compare the performance against the different classification techniques such as support vector machine, artificial neural network (ANN) and CNN. Second, the computational cost of the hybrid WOA_APSO is compared with the standard WOA and APSO algorithms. The experimental result reveals that the proposed algorithm is capable of automatic lung tumor detection and it outperforms the other state-of-the-art methods on standard quality measures such as accuracy (97.18%), sensitivity (97%) and specificity (98.66%). The results reported in this paper are encouraging; hence, these results will motivate other researchers to explore more in this direction.

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“…Multilayer Perceptrons are a kind of artificial neural networks (ANN) commonly employed for pattern recognition and classification problems [30]- [32]. ANNs possess the ability to recognize relationships between input and output variables via training.…”

Section: Multilayer Perceptron Neural Networkmentioning

confidence: 99%

Autoregressive Modeling Based Empirical Mode Decomposition (EMD) for Epileptic Seizures Detection Using EEG Signals

Djemili¹,

Boubchir²

2019

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Epilepsy is a neurological disorder affecting several millions of humans on earth. Epileptic seizures provoked in major cases by sudden electrical discharges of tremendous brain cells could not be predicted. Hence, automatic seizures detection and classification based on the analysis of electroencephalographic (EEG) signals becomes essential. The purpose of this paper is to propose a new feature extraction method using empirical mode decomposition (EMD) and a multilayer perceptron neural network (MLPNN). The EMD algorithm decomposes a time segment EEG into intrinsic mode functions (IMFs) on which autoregressive (AR) parameters are extracted, combined and fed to the MLPNN classifier. Experimental results carried out on a publicly available dataset, comprising normal, interictal and ictal EEG signals achieved classification accuracy up to 98 %. The outcome of this research is mainly intended to aid practioners in the diagnosis of epileptic portions in the EEG recordings.

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Iris tissue recognition based on GLDM feature extraction and hybrid MLPNN-ICA classifier

Cited by 37 publications

References 26 publications

Machine Learning Models for Sarcopenia Identification Based on Radiomic Features of Muscles in Computed Tomography

Machine Learning Models for Sarcopenia Identification Based on Radiomic Features of Muscles in Computed Tomography

Hybrid bio-inspired algorithm and convolutional neural network for automatic lung tumor detection

Autoregressive Modeling Based Empirical Mode Decomposition (EMD) for Epileptic Seizures Detection Using EEG Signals

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