Texture Features Selection for Masses Detection In Digital Mammogram

Khuzi, Azlindawaty Mohd; Besar, Rosli; Zaki, Wan Mimi Diyana Wan

doi:10.1007/978-3-540-69139-6_157

Cited by 21 publications

(6 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…

italicEnergy goodbreak= \sum_{i, j = 1}^{N - 1} {(P_{i, j})}^{2}

italicEntrophy goodbreak= \sum_{i, j = 1}^{N - 1} goodbreak- \ln ((), P_{i, j}) P_{i, j}

italicContrast goodbreak= \sum_{i, j = 1}^{N - 1} ((), P_{i, j}) {(i - j)}^{2}

italicCorrelation goodbreak= \sum_{i, j = 1}^{N - 1} ((), P_{i, j}) \frac{((), i goodbreak- μ) ((), j goodbreak- μ)}{σ^{2}}

italicHomogeneity goodbreak= \sum_{i, j = 0}^{N - 1} \frac{P_{i, j}}{1 + {(i - j)}^{2}}

where P i, j is normalized symmetrical, N is number of grey levels, μ is mean intensity, σ is variance of intensity. The textural features were calculated using various equations used in (Castellano et al, 2004; Esgiar et al, 2002; Guru et al, 2010; Haider et al, 2017; Khalvati et al, 2015; Khuzi et al, 2008; Yu et al, 2017). The prediction size, radius of the prediction area, image equivalence, and dispersion were calculated using morphological features shown in Equations () and ().…”

Section: Methodsmentioning

confidence: 99%

“…where P i, j is normalized symmetrical, N is number of grey levels, μ is mean intensity, σ is variance of intensity. The textural features were calculated using various equations used in (Castellano et al, 2004;Esgiar et al, 2002;Guru et al, 2010;Haider et al, 2017;Khalvati et al, 2015;Khuzi et al, 2008;Yu et al, 2017). The prediction size, radius of the prediction area, image equivalence, and dispersion were calculated using morphological features shown in Equations ( 6) and ( 7).…”

Section: Pre-processing Of Datasetmentioning

confidence: 99%

See 1 more Smart Citation

A hybrid learning approach for the stage‐wise classification and prediction of COVID‐19 X‐ray images

et al. 2021

View full text Add to dashboard Cite

Background The COVID‐19 pandemic has precipitated global apprehensions about increased fatalities and raised concerns about gaps in healthcare infrastructure and accessibility the world over. Consequently, the importance of timely prediction and treatment of the disease to reduce transmission and mortality rates cannot be emphasized enough. Various symptoms of the disease have been identified as it progresses from the time it is contracted. COVID‐19 has been found to internally affect the lungs, and the four progressive stages of the infection can be categorized as mild, moderate, severe, and critical. Therefore, an accurate analysis of the current stage of the disease that can help predict its progression has become critical. X‐ray imaging has been found to be an effective screening procedure for predicting the various stages of this epidemic. Although many different approaches using machine learning, as well as deep learning were utilized to predict and classify diseases in general, till date, such an approach has not been used to predict the various stages of COVID‐19 by using X‐ray imaging to identify and classify those stages. Materials and method The proposed hybrid method used three public datasets for its implementation. In this work, extensive images were used for the purposes of testing and training. The dataset‐1 consists of 1200 COVID‐19 as well as 1200 Non‐COVID‐19 images, while dataset‐2 used 700 COVID‐19 as well as 700 Non‐COVID‐19 images, and finally, dataset‐III utilized 1900 COVID‐19 as well as 1900 Non‐COVID‐19 images for purposes of testing and training. The proposed work undertook the task of pre‐processing using textual and morphological features, while the segmentation and prediction of COVID‐19 as well as Non‐COVID‐19 images were undertaken using VGG‐16 with light GBM for better prediction and handing of huge datasets, and finally, the classification of the various stages of COVID‐19 images was performed using Deep Belief Network. Results The outcomes of the proposed work were subjected to several iterations which were then compared using different parameters such as accuracy, specificity, and sensitivity. In general, the prediction and grouping of the various stages of COVID‐19 by using affected images were found to be 99.2%, 99.4% and 99.5%, respectively. The bacterial pneumonia prediction rates were observed to be 98.5%, 99.4% and 98.3%, respectively. The average classification of the stages were found to be 98.1%, 98.6% and 98.3%, while the combined multi‐classification prediction rates were observed to be 98.6%, 99.1% and 98.7%, respectively.

show abstract

“…

italicEnergy goodbreak= \sum_{i, j = 1}^{N - 1} {(P_{i, j})}^{2}

italicEntrophy goodbreak= \sum_{i, j = 1}^{N - 1} goodbreak- \ln ((), P_{i, j}) P_{i, j}

italicContrast goodbreak= \sum_{i, j = 1}^{N - 1} ((), P_{i, j}) {(i - j)}^{2}

italicCorrelation goodbreak= \sum_{i, j = 1}^{N - 1} ((), P_{i, j}) \frac{((), i goodbreak- μ) ((), j goodbreak- μ)}{σ^{2}}

italicHomogeneity goodbreak= \sum_{i, j = 0}^{N - 1} \frac{P_{i, j}}{1 + {(i - j)}^{2}}

Section: Methodsmentioning

confidence: 99%

Section: Pre-processing Of Datasetmentioning

confidence: 99%

A hybrid learning approach for the stage‐wise classification and prediction of COVID‐19 X‐ray images

et al. 2021

View full text Add to dashboard Cite

show abstract

“…To compute the GLCM, we utilized the distance, d = {1, 2, 3, 4}, and angle, θ = {0 • , 45 • , 90 • and 135 • }, for directions. Consider the pixel probability, P(i, j, d, θ), indicates the two pixels' probability separated by a particular distance having gray-levels i and j [33][34][35]. The GLCM-based texture features were composed of contrast, sum of square variance, cluster shade [36], correlation [37], and two values of homogeneity [37][38][39].…”

Section: Gray-level Co-occurrence Matrix (Glcm)mentioning

confidence: 99%

Lung Cancer Prediction Using Robust Machine Learning and Image Enhancement Methods on Extracted Gray-Level Co-Occurrence Matrix Features

et al. 2022

View full text Add to dashboard Cite

In the present era, cancer is the leading cause of demise in both men and women worldwide, with low survival rates due to inefficient diagnostic techniques. Recently, researchers have been devising methods to improve prediction performance. In medical image processing, image enhancement can further improve prediction performance. This study aimed to improve lung cancer image quality by utilizing and employing various image enhancement methods, such as image adjustment, gamma correction, contrast stretching, thresholding, and histogram equalization methods. We extracted the gray-level co-occurrence matrix (GLCM) features on enhancement images, and applied and optimized vigorous machine learning classification algorithms, such as the decision tree (DT), naïve Bayes, support vector machine (SVM) with Gaussian, radial base function (RBF), and polynomial. Without the image enhancement method, the highest performance was obtained using SVM, polynomial, and RBF, with accuracy of (99.89%). The image enhancement methods, such as image adjustment, contrast stretching at threshold (0.02, 0.98), and gamma correction at gamma value of 0.9, improved the prediction performance of our analysis on 945 images provided by the Lung Cancer Alliance MRI dataset, which yielded 100% accuracy and 1.00 of AUC using SVM, RBF, and polynomial kernels. The results revealed that the proposed methodology can be very helpful to improve the lung cancer prediction for further diagnosis and prognosis by expert radiologists to decrease the mortality rate.

show abstract

“…Out of these four multi-wave performed well, and normal shape attributes is superior to Haralick and wavelet. KNN classifier is used for detecting malignancy of given microcalcification clusters Khuzi et al [36] investigate the texture attributes skill to well known between the unreasonable expectation of masses and the non-masses area in screening mammogram. Authors are used GLCM for extracting different features in mammogram image.…”

Section: Related Workmentioning

confidence: 99%

Imaging & Machine Learning Techniques Used for Early Identification of Cancer in Breast Mammogram

Tripathy¹,

Swarnkar²

2019

IJRTE

View full text Add to dashboard Cite

 Abstract: Breast cancer has become a major concern of women health throughout the world and has an important cause of death among women. The important radiographic signs of cancer are the masses visible in the breast. In the initial stage, the masses in the women breast are very strenuous to detect. In many cases, it has been proven that a manual attempt of treatment methods are time consuming and inefficient. Hence there is a basic demand for well-planned methods for diagnosis of the cancerous cells with minimal human participation resulting high in precision. Mammography has been proven as an efficient technique for the identification of cancer in women breast. Automated detection of masses in breast mammogram is the major goal in the identification of cancer in women breast. Machine learning techniques can be used as an effective mechanism by the physician for the early detection of cancer in the breast. By early recognition of malignancy in the breast, patients will get treatment right from the initial stage of cancer which can save their lives. Keywords : breast cancer, computer aided detection (CAD), mammogram, machine learning (ML), region of interest (ROI) .

show abstract

Texture Features Selection for Masses Detection In Digital Mammogram

Cited by 21 publications

References 3 publications

A hybrid learning approach for the stage‐wise classification and prediction of COVID‐19 X‐ray images

A hybrid learning approach for the stage‐wise classification and prediction of COVID‐19 X‐ray images

Lung Cancer Prediction Using Robust Machine Learning and Image Enhancement Methods on Extracted Gray-Level Co-Occurrence Matrix Features

Imaging & Machine Learning Techniques Used for Early Identification of Cancer in Breast Mammogram

Contact Info

Product

Resources

About