Application of Feature Extraction for Breast Cancer using One Order Statistic, GLCM, GLRLM, and GLDM

Novitasari, Dian Candra Rini; Lubab, Ahmad; Sawiji, Asri; Asyhar, Ahmad Hanif

doi:10.25046/aj040413

Cited by 33 publications

(14 citation statements)

References 20 publications

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“…Future research is expected to increase the sensitivity and accuracy with other feature extraction methods such as Gray Level Run-Length Matrix (GLRLM). In a study conducted by [34], GLRLM had better accuracy quality than the GLCM method. The future work is by applying ELM development methods, namely the Kernel Extreme Learning Machine (K-ELM) [35] and Multi-Layer Extreme Learning Machine (MLLEM) [36].…”

Section: Resultsmentioning

confidence: 94%

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Image X-Ray Classification for COVID-19 Detection Using GCLM-ELM

2021

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COVID-19 is a disease or virus that has recently spread worldwide. The disease has also taken many casualties because the virus is notoriously deadly. An examination can be carried out using a chest X-Ray because it costs cheaper compared to swab and PCR tests. The data used in this study was chest X-Ray image data. Chest X-Ray images can be identified using Computer-Aided Diagnosis by utilizing machine learning classification. The first step was the preprocessing stage and feature extraction using the Gray Level Co-Occurrence Matrix (GLCM). The result of the feature extraction was then used at the classification stage. The classification process used was Extreme Learning Machine (ELM). Extreme Learning Machine (ELM) is one of the artificial neural networks with advanced feedforward which has one hidden layer called Single Hidden Layer Feedforward Neural Networks (SLFNs). The results obtained by GLCM feature extraction and classification using ELM achieved the best accuracy of 91.21%, the sensitivity of 100%, and the specificity of 91% at 135° rotation using linear activation function with 15 hidden nodes.

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Section: Resultsmentioning

confidence: 94%

“…Table 2 shows the data used in this study. The data was taken on 03 April 2020 from [32][33][34][35]. The data increased every week so that when retrieving at different times the number of data increased.…”

Section: Datamentioning

confidence: 99%

Image X-Ray Classification for COVID-19 Detection Using GCLM-ELM

2021

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“…The testing is performed on different parameters to identify the best combination for determining the robustness of the experiment. The various parameter are as follows: Layer neurons: [5,10]…”

Section: Convolutional Neural Network Is a Deep Learningmentioning

confidence: 99%

“…The global thresholding and morphological post-processing operation are performed for detecting the tumored region from surrounding and segmenting the lung region nodule. The different statistical features, textural features, shape and geometrical-based features are extracted from the segmented region [10]. The number of features is prominently more in neuroimaging, so the technique used for feature extraction is Gray-Level Co-occurrence Matrix (GLCM), Gray-Level Run-Length Matrix (GLRLM), Histogram features, Gray-Level Dependence Matrix (GLDM) and Local Binary Pattern (LBP) [11,12].…”

Section: Introductionmentioning

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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“…The first process to do is extracting features. The feature extraction process is the images classifying based on the characteristics of the images [9] by calculating the value of the results of segmentation using a histogram. The results are then calculated using the gray level co-occurrence matrix (GLCM) method.…”

Section: Introductionmentioning

confidence: 99%

Gray level co-occurrence matrix feature extraction and histogram in breast cancer classification with ultrasonographic imagery

Djunaidi¹,

Agtriadi²,

Kuswardani³

et al. 2021

IJEECS

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One way to detect breast cancer is using the Ultrasonography (USG) procedure, but the ultrasound image is susceptible to the noise speckles so that the interpretation and diagnosis results are different. This paper discusses the classification of breast cancer ultrasound images that aims to improve the accuracy of the identification of the type and level of cancer malignancies based on the features of its texture. The feature extraction process uses a <em>histogram</em> which then the results are calculated using the Gray Level Co-Occurrence Matrix (GLCM). The results of the two extraction features are then classified using K-Nearest Neighbors (KNN) to obtain accurate figures from those images. The results of this study is that the accuracy in detecting cancer types is 80%.

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Application of Feature Extraction for Breast Cancer using One Order Statistic, GLCM, GLRLM, and GLDM

Cited by 33 publications

References 20 publications

Image X-Ray Classification for COVID-19 Detection Using GCLM-ELM

Image X-Ray Classification for COVID-19 Detection Using GCLM-ELM

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

Gray level co-occurrence matrix feature extraction and histogram in breast cancer classification with ultrasonographic imagery

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