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

Arafa, A. A.; El-Sokary, Nesma; Asad, Ahmed H.; Hefny, Hesham A.

doi:10.21608/ajnsa.2019.7274.1170

Cited by 10 publications

(3 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The performance of this study was evaluated using MIAS dataset of images and it achieved an accuracy rate of 90%. In 2019, A. Arafa et al introduced a CAD system for the breast cancer detection [14]. In the pre-processing step, the area including the breast region is automatically picked and artifacts as well as pectoral muscle were removed.…”

Section: Literature Reviewmentioning

confidence: 99%

Classification System for Early Breast Cancer Diagnosis using Machine Learning

Aziz,

Abdulla

2024

Preprint

View full text Add to dashboard Cite

Morbidity and even mortality are common outcomes of breast cancer, which is one of the most common diseases affecting women worldwide. Breast cancer is challenging, expensive, and takes a long time to diagnose manually by radiologist. Since it gets beyond all of the drawbacks of manual diagnosis, an automatic/computer-based diagnosis of breast cancer might be thought of as an alternative to manual diagnosis. Utilizing image processing techniques, computer-based diagnostic systems process breast images from mammograms. This study aims to suggest a computer-based diagnostic system for breast cancer by using machine learning to classify the input mammography image into three classes: normal, benign, and malignant. The suggested system comprises of a certain of steps. The input image is initially pre-processed to remove labels and enhance image quality using median filter and adaptive histogram equalization. The next step entails applying the threshold segmentation technique to segment the cancer cells in order to isolate the region of interest (ROI). The Gray Level Run Length Matrix (GLRLM) feature extraction technique is then implemented to extract texture features from the segmented ROI. Consequently, on the basis of the extracted features, Support Vector Machine (SVM) and k-Nearest Neighbor (kNN) classifier techniques are employed to classify the segmented region as normal, benign, and malignant. The performance of the proposed system was examined via extensive experiments conducted on the well-known Mammographic Image Analysis Society (MIAS) dataset of mammography images. The experimental findings reveal that the proposed system outperforms existing systems, which attained a 98.6% accuracy rate.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Classification System for Early Breast Cancer Diagnosis using Machine Learning

Aziz,

Abdulla

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…In 2019, Arafa et al introduced a technique for the breast cancer detection [14]. In the pre-processing step, just the area including the breast region is automatically picked and artifacts as well as pectoral muscle were removed.…”

Section: Sarosa Et Al In 2019 Designed a Breast Cancer Diagnosis Tech...mentioning

confidence: 99%

Computer-Aided Diagnosis for the Early Breast Cancer Detection

Aziz¹,

Abdulla²

2023

UHD J SCI TECH

View full text Add to dashboard Cite

Computer-aided diagnosis (CAD) system is a prominent tool for the detection of different forms of diseases, especially cancers, based on medical imaging. Digital image processing is a critical in the processing and analysis of medical images for the disease diagnosis and detection. This study introduces a CAD system for detecting breast cancer. Once the breast region is segmented from the mammograms image, certain texture and statistical features are extracted. GLRLM feature extraction technique is implemented to extracted texture features. On the other hand, statistical features such as skewness, mean, entropy, and standard deviation are extracted. Consequently, on the basis of the extracted features, SVM and kNN classifier techniques are utilized to classify the segmented region as normal or abnormal. The performance of the proposed approach has been investigated via extensive experiments conducted on the well-known MIAS dataset of mammography images. The experimental findings show that the suggested approach outperforms other existing approaches, with an accuracy rate of 99.7%.

show abstract

“…Several techniques had been used to classify BC using the MIAS database 9 , such as Bayesian Neural Networks 10 , Relevance Feedback (RF) and Relevance Feedback extreme learning machine (RF-ELM) 11 , optimized kernel extreme learning machine (KELM) 12 , K-nearest neighbor (KNN) 13 , Discrimination Potentiality (DP) 14 , SVM 15 – 17 , and DL CNN 18 . DL, a component of machine learning algorithms, is primarily focused on automatically extracting and classifying image features.…”

Section: Introductionmentioning

confidence: 99%

Efficient breast cancer mammograms diagnosis using three deep neural networks and term variance

Elkorany

Elsharkawy

2023

Sci Rep

View full text Add to dashboard Cite

Breast cancer (BC) is spreading more and more every day. Therefore, a patient's life can be saved by its early discovery. Mammography is frequently used to diagnose BC. The classification of mammography region of interest (ROI) patches (i.e., normal, malignant, or benign) is the most crucial phase in this process since it helps medical professionals to identify BC. In this paper, a hybrid technique that carries out a quick and precise classification that is appropriate for the BC diagnosis system is proposed and tested. Three different Deep Learning (DL) Convolution Neural Network (CNN) models—namely, Inception-V3, ResNet50, and AlexNet—are used in the current study as feature extractors. To extract useful features from each CNN model, our suggested method uses the Term Variance (TV) feature selection algorithm. The TV-selected features from each CNN model are combined and a further selection is performed to obtain the most useful features which are sent later to the multiclass support vector machine (MSVM) classifier. The Mammographic Image Analysis Society (MIAS) image database was used to test the effectiveness of the suggested method for classification. The mammogram's ROI is retrieved, and image patches are assigned to it. Based on the results of testing several TV feature subsets, the 600-feature subset with the highest classification performance was discovered. Higher classification accuracy (CA) is attained when compared to previously published work. The average CA for 70% of training is 97.81%, for 80% of training, it is 98%, and for 90% of training, it reaches its optimal value. Finally, the ablation analysis is performed to emphasize the role of the proposed network’s key parameters.

show abstract

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

Cited by 10 publications

References 16 publications

Classification System for Early Breast Cancer Diagnosis using Machine Learning

Classification System for Early Breast Cancer Diagnosis using Machine Learning

Computer-Aided Diagnosis for the Early Breast Cancer Detection

Efficient breast cancer mammograms diagnosis using three deep neural networks and term variance

Contact Info

Product

Resources

About