Effective detection of mass abnormalities and its classification using multi-SVM classifier with digital mammogram images

Jothilakshmi, G. R.; Raaza, Arun

doi:10.1109/icccsp.2017.7944090

Cited by 20 publications

(10 citation statements)

References 5 publications

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“…(6) as 87.5%. Table ( 1) shows the comparison of tumors detection in previously reported data [6,7]. Jothilakshmi and Raaza [7] selected 50 mammogram images that were previously known as abnormal and detected the tumor type at a rate of 94%.…”

Section: Resultsmentioning

confidence: 99%

“…Table ( 1) shows the comparison of tumors detection in previously reported data [6,7]. Jothilakshmi and Raaza [7] selected 50 mammogram images that were previously known as abnormal and detected the tumor type at a rate of 94%. Ismahan et al, [6]used two techniques for segmentation and test type of abnormality at the rate of 90%.On the other hand, running the proposed CAD system for detecting the type of abnormality achieve an accuracy of 92.5% as shown in Table (1).…”

Section: Resultsmentioning

confidence: 99%

“…Ismahan et al, [6]used two techniques for segmentation and test type of abnormality at the rate of 90%.On the other hand, running the proposed CAD system for detecting the type of abnormality achieve an accuracy of 92.5% as shown in Table (1). The proposed CAD system has the advantages over the methods in [6,7] and other, that it is fully automated in all its stages and uses less complex and fast computation procedure without any radiologist contribution.…”

Section: Resultsmentioning

confidence: 99%

“…Jothilakshmi, G.R., and Arun Raaza [7] proposed an effective method for the detection of masses in digital mammogram images and classification using multi-SVM classifier. They used a regionbased segmentation method for extracting the ROI from mammogram images.…”

Section: Literature Surveymentioning

confidence: 99%

See 3 more Smart Citations

Computer-Aided Detection System for Breast Cancer Based on GMM and SVM

Arafa

El-Sokary

Asad

et al. 2019

Arab Journal of Nuclear Sciences and Applications

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Region-of-interest (ROI) segmentation is an important critical step and challenging task in the evolution of computer-aided detection (CAD) system for breast cancer. The discovery of breast cancer in early stages can save many women lives.However, most of the early detection systems are costly in terms of complexity,price and processing time; that make it unsuited for developing countries.The digital mammography is proven to be one of the most important diagnostic techniques for breast cancer tumors. Therefore, this work proposes a CADsystem for breast cancer detectionfrom digital mammography based on GaussianMixture Model(GMM) followed bySupport Vector Machine (SVM).The best contribution of our proposed system is the usage of GMM for the first time in the literature for mammogram images segmentation into ROI areas.Besides, the discrimination betweenthe three classes of tissues as normal,benign ormalignant, isused without previous knowledge of mammogram images'type. Moreover, the proposed system is fully automated in all of its stages with reduced computation compared with recent used methods. Hence, it offers a suitable early detection system to our country regarding moneywise, timewise, and reduced complexity.A non-linear multi-class SVMis used for classifying the ROI into three classes: normal, benignor malignant tissue.The experiments show overall average classification accuracy of 90% for detecting normal, malignant or benignonrandomly chosen90 cases from the benchmark mini-MIAS dataset. On the other hand, the proposed method achieves 92.5% accuracy when classifying the benign from malignant cases. The confusion matrix was used to assess the proposed CAD system overall performance as it is considered to be a good evaluation metric method.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

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Computer-Aided Detection System for Breast Cancer Based on GMM and SVM

Arafa

El-Sokary

Asad

et al. 2019

Arab Journal of Nuclear Sciences and Applications

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“…Jothilakshmi and Raaza [100] proposed an effective method for detecting mass abnormalities and classifying images as benign versus malignant via multi-SVM. The region segmentation method was applied to segmented mammogram images based on the split and merge techniques.…”

Section: Mammograms Breast Cancer Segmentation-based Region Methods (Rm) Dehghani and Dezfoolimentioning

confidence: 99%

Breast Cancer Segmentation Methods: Current Status and Future Potentials

Michael

et al. 2021

BioMed Research International

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Early breast cancer detection is one of the most important issues that need to be addressed worldwide as it can help increase the survival rate of patients. Mammograms have been used to detect breast cancer in the early stages; if detected in the early stages, it can drastically reduce treatment costs. The detection of tumours in the breast depends on segmentation techniques. Segmentation plays a significant role in image analysis and includes detection, feature extraction, classification, and treatment. Segmentation helps physicians quantify the volume of tissue in the breast for treatment planning. In this work, we have grouped segmentation methods into three groups: classical segmentation that includes region-, threshold-, and edge-based segmentation; machine learning segmentation; and supervised and unsupervised and deep learning segmentation. The findings of our study revealed that region-based segmentation is frequently used for classical methods, and the most frequently used techniques are region growing. Further, a median filter is a robust tool for removing noise. Moreover, the MIAS database is frequently used in classical segmentation methods. Meanwhile, in machine learning segmentation, unsupervised machine learning methods are more frequently used, and U-Net is frequently used for mammogram image segmentation because it does not require many annotated images compared with other deep learning models. Furthermore, reviewed papers revealed that it is possible to train a deep learning model without performing any preprocessing or postprocessing and also showed that the U-Net model is frequently used for mammogram segmentation. The U-Net model is frequently used because it does not require many annotated images and also because of the presence of high-performance GPU computing, which makes it easy to train networks with more layers. Additionally, we identified mammograms and utilised widely used databases, wherein 3 and 28 are public and private databases, respectively.

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Proposing a Framework to Analyze Breast Cancer in Mammogram Images Using Global Thresholding, Gray Level Co‐Occurrence Matrix, and Convolutional Neural Network (CNN)

Dixit¹,

Singh²

2022

Advances in Data Science and Analytics

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Effective detection of mass abnormalities and its classification using multi-SVM classifier with digital mammogram images

Cited by 20 publications

References 5 publications

Computer-Aided Detection System for Breast Cancer Based on GMM and SVM

Computer-Aided Detection System for Breast Cancer Based on GMM and SVM

Breast Cancer Segmentation Methods: Current Status and Future Potentials

Proposing a Framework to Analyze Breast Cancer in Mammogram Images Using Global Thresholding, Gray Level Co‐Occurrence Matrix, and Convolutional Neural Network (CNN)

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