scholarly journals Breast Cancer Classification using SVM Classifier

2019 ◽  
Vol 8 (4) ◽  
pp. 527-529
Keyword(s):  
Breast Cancer ◽  
Breast Imaging ◽  
Low Dose ◽  
Risk Groups ◽  
Morphological Processing ◽  
Svm Classifier ◽  
X Rays ◽  
Difference Of Gaussian ◽  
Prime Objective ◽  
Aided Diagnosis

Early detection of breast cancer is believed to enhance the chance of survival. Mammography is the best available breast imaging technique at present which uses low-dose x-rays for detecting the breast cancer early before the symptoms are experienced. The most commonly present abnormalities in mammograms that may indicate the breast malignancy are masses and microcalcifications. The prime objective of this research is to increase the diagnostic accuracy of the detection of breast cancer malignancy in Computer Aided Diagnosis (CAD) systems by developing image processing algorithms and to categorize the women into different risk groups. The evaluation of SVM classifier has been considered. Initially, tumors have been detected from mammograms with the aid of morphological processing of breast images. Then classification is done by SVM classifier using the most dominant features namely GLRLM and Difference of Gaussian (DoG) features, which have been extracted from the selected region. The algorithm has achieved an accuracy of 89.11% using SVM classifier.

scholarly journals King Abdulaziz University Breast Cancer Mammogram Dataset (KAU-BCMD)

Data ◽  
2021 ◽  
Vol 6 (11) ◽  
pp. 111
Author(s):  
Asmaa S. Alsolami ◽  
Wafaa Shalash ◽  
Wafaa Alsaggaf ◽  
Sawsan Ashoor ◽  
Haneen Refaat ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Imaging ◽  
Tumor Development ◽  
Cancer Risk Factors ◽  
Cad Systems ◽  
Saudi Women ◽  
Breast Cancer Risk Factors ◽  
King Abdulaziz University ◽  
The Right ◽  
Aided Diagnosis

The current era is characterized by the rapidly increasing use of computer-aided diagnosis (CAD) systems in the medical field. These systems need a variety of datasets to help develop, evaluate, and compare their performances fairly. Physicians indicated that breast anatomy, especially dense ones, and the probability of breast cancer and tumor development, vary highly depending on race. Researchers reported that breast cancer risk factors are related to culture and society. Thus, there is a massive need for a local dataset representing breast cancer in our region to help develop and evaluate automatic breast cancer CAD systems. This paper presents a public mammogram dataset called King Abdulaziz University Breast Cancer Mammogram Dataset (KAU-BCMD) version 1. To our knowledge, KAU-BCMD is the first dataset in Saudi Arabia that deals with a large number of mammogram scans. The dataset was collected from the Sheikh Mohammed Hussein Al-Amoudi Center of Excellence in Breast Cancer at King Abdulaziz University. It contains 1416 cases. Each case has two views for both the right and left breasts, resulting in 5662 images based on the breast imaging reporting and data system. It also contains 205 ultrasound cases corresponding to a part of the mammogram cases, with 405 images as a total. The dataset was annotated and reviewed by three different radiologists. Our dataset is a promising dataset that contains different imaging modalities for breast cancer with different cancer grades for Saudi women.

scholarly journals Breast cancer detection using deep convolutional neural networks and support vector machines

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6201 ◽  
Author(s):  
Dina A. Ragab ◽  
Maha Sharkas ◽  
Stephen Marshall ◽  
Jinchang Ren
Keyword(s):  
Breast Cancer ◽  
Breast Imaging ◽  
Input Data ◽  
Data Augmentation ◽  
Area Under The Curve ◽  
Screening Mammography ◽  
Support Vector ◽  
Svm Classifier ◽  
Deep Convolutional Neural Networks ◽  
Cad System

It is important to detect breast cancer as early as possible. In this manuscript, a new methodology for classifying breast cancer using deep learning and some segmentation techniques are introduced. A new computer aided detection (CAD) system is proposed for classifying benign and malignant mass tumors in breast mammography images. In this CAD system, two segmentation approaches are used. The first approach involves determining the region of interest (ROI) manually, while the second approach uses the technique of threshold and region based. The deep convolutional neural network (DCNN) is used for feature extraction. A well-known DCNN architecture named AlexNet is used and is fine-tuned to classify two classes instead of 1,000 classes. The last fully connected (fc) layer is connected to the support vector machine (SVM) classifier to obtain better accuracy. The results are obtained using the following publicly available datasets (1) the digital database for screening mammography (DDSM); and (2) the Curated Breast Imaging Subset of DDSM (CBIS-DDSM). Training on a large number of data gives high accuracy rate. Nevertheless, the biomedical datasets contain a relatively small number of samples due to limited patient volume. Accordingly, data augmentation is a method for increasing the size of the input data by generating new data from the original input data. There are many forms for the data augmentation; the one used here is the rotation. The accuracy of the new-trained DCNN architecture is 71.01% when cropping the ROI manually from the mammogram. The highest area under the curve (AUC) achieved was 0.88 (88%) for the samples obtained from both segmentation techniques. Moreover, when using the samples obtained from the CBIS-DDSM, the accuracy of the DCNN is increased to 73.6%. Consequently, the SVM accuracy becomes 87.2% with an AUC equaling to 0.94 (94%). This is the highest AUC value compared to previous work using the same conditions.

Effect of GLCM Texture Features on the Medio-Lateral Oblique (MLO) View of Digital Mammograms for Breast Cancer Detection

2020 ◽  
Vol 9 (2) ◽  
pp. 25-44
Author(s):  
Usha N. ◽  
Sriraam N. ◽  
Kavya N. ◽  
Bharathi Hiremath ◽  
Anupama K Pujar ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Classification Accuracy ◽  
Breast Imaging ◽  
Texture Features ◽  
Support Vector ◽  
Risk Of Death ◽  
Automated Method ◽  
Occurrence Matrix ◽  
Aided Diagnosis ◽  
Mammogram Images

Breast cancer is one among the most common cancers in women. The early detection of breast cancer reduces the risk of death. Mammograms are an efficient breast imaging technique for breast cancer screening. Computer aided diagnosis (CAD) systems reduce manual errors and helps radiologists to analyze the mammogram images. The mammogram images are typically in two views, cranial-caudal (CC) and medio lateral oblique (MLO) views. MLO contains pectoral muscles (chest muscles) at the upper right or left corner of the image. In this study, it was removed by using a semi-automated method. All the normal and abnormal images were filtered and enhanced to improve the quality. GLCM (Gray Level Co-occurrence Matrix) texture features were extracted and analyzed by changing the number of features in a feature set. Linear Support Vector Machine (LSVM) was used as classifier. The classification accuracy was improved as the number of features in GLCM feature set increases. Simulation results show an overall classification accuracy of 96.7% with 19 GLCM features using SVM classifiers.

scholarly journals NanoChest-Net: A Simple Convolutional Network for Radiological Studies Classification

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 775
Author(s):  
Juan Eduardo Luján-García ◽  
Yenny Villuendas-Rey ◽  
Itzamá López-Yáñez ◽  
Oscar Camacho-Nieto ◽  
Cornelio Yáñez-Márquez
Keyword(s):  
Breast Cancer ◽  
State Of The Art ◽  
X Rays ◽  
Huge Amount ◽  
Convolutional Network ◽  
X Ray ◽  
Learning Techniques ◽  
Computer Aided ◽  
Aided Diagnosis ◽  
Radiological Studies

The new coronavirus disease (COVID-19), pneumonia, tuberculosis, and breast cancer have one thing in common: these diseases can be diagnosed using radiological studies such as X-rays images. With radiological studies and technology, computer-aided diagnosis (CAD) results in a very useful technique to analyze and detect abnormalities using the images generated by X-ray machines. Some deep-learning techniques such as a convolutional neural network (CNN) can help physicians to obtain an effective pre-diagnosis. However, popular CNNs are enormous models and need a huge amount of data to obtain good results. In this paper, we introduce NanoChest-net, which is a small but effective CNN model that can be used to classify among different diseases using images from radiological studies. NanoChest-net proves to be effective in classifying among different diseases such as tuberculosis, pneumonia, and COVID-19. In two of the five datasets used in the experiments, NanoChest-net obtained the best results, while on the remaining datasets our model proved to be as good as baseline models from the state of the art such as the ResNet50, Xception, and DenseNet121. In addition, NanoChest-net is useful to classify radiological studies on the same level as state-of-the-art algorithms with the advantage that it does not require a large number of operations.

scholarly journals Intelligent Classification Technique for Breast Cancer

2019 ◽  
Vol 8 (6) ◽  
pp. 2313-2316
Keyword(s):  
Breast Cancer ◽  
Developed Countries ◽  
Invasive Disease ◽  
Support Vector ◽  
Svm Classifier ◽  
Image Processing Algorithm ◽  
Non Invasive ◽  
Computer Aided ◽  
Aided Diagnosis

Breast cancer is also a leading cause of cancer death in the less developed countries of the world. This is partly because a shift in lifestyles is causing an increase in incidence. Breast cancer originates from the inner lining of milk ducts/lobes either in the form of invasive or non invasive disease in general. Mammography, particularly with Computer-Aided Detection (CAD), can now produce images detailed enough for diagnostic purposes, and digital mammography allows transmission of 3-dimensionssal images over long distances. The aim for the system is to design a Computer Aided Diagnosis systematic tool for perceiving non cancerous and perilous (cancer causing) mammogram. The aim of the research is proposed to develop an image processing algorithm for an automatic detection and classification of breast lesions accurately. CAD tool helped radiologist in expanding his assurance accuracy. Support vector machine (SVM) classifier is used to discriminate the tumors into benign or malignant. Incorporate best features of the find out that has significant responsibility in achieving the perfect turnout which are then designated and associated with ANN to train and classify.

scholarly journals Breast Cancer Detection, Diagnosis, and Prediction

2020 ◽  
Vol 9 (6) ◽  
pp. 38-42
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Breast Imaging ◽  
Malignant Tumors ◽  
Region Of Interest ◽  
Breast Cancer Detection ◽  
Support Vector ◽  
Svm Classifier ◽  
Cad System ◽  
Mammogram Images

The early detection, diagnosis, prediction, and treatment of breast cancer are challenginghealthcare problems. This study focuses on outlining the traditional and trending techniques used for breast cancer detection, diagnosis, and prediction, including trending noninvasive, nonionizing, and biomarker genetic techniques.In addition, a Computer Aided Detection (CAD) is introduced to classify benign and malignant tumors in mammograms. This CAD system involves three steps. First, the Region of Interest (ROI) that includesthe tumor is identified using a threshold-based method. Second, a deep learning Convolutional Neural Network (CNN) processes the ROI to extract relevant mammogram features. Finally, a Support Vector Machine (SVM) classifier is used to decode two classes of mammogram structures (i.e., Benign (B), and Malignant (M) nodules). The training processes and implementations were carried out using 2800 mammogram images taken from the Curated Breast Imaging Subset of DDSM (CBIS-DDSM). Results have shown that the accuracy of CNN-SVM system achieves 85.1% using AlexNet CNN. Comparison with related work shows the promise of the proposed CAD system

Computer-Aided Diagnosis in Breast Imaging

2009 ◽  
pp. 142-159 ◽  
Author(s):  
Lena Costaridou ◽  
Spyros Skiadopoulos ◽  
Anna Karahaliou ◽  
Nikolaos Arikidis ◽  
George Panayiotakis
Keyword(s):  
Breast Cancer ◽  
Breast Imaging ◽  
Multimodality Imaging ◽  
Computer Aided Diagnosis ◽  
Tissue Properties ◽  
Computer Aided ◽  
Clinical Tools ◽  
Magnetic Resonance Imaging Mri ◽  
Microcalcification Clusters ◽  
Aided Diagnosis

Breast cancer is the most common cancer in women worldwide. Mammography is currently the most effective modality in detecting breast cancer, challenged by the presence of dense breast parenchyma with relatively low specificity in distinguishing malignant from benign lesions. Breast ultrasound and Magnetic Resonance Imaging (MRI) are significant adjuncts to mammography providing additional diagnostic information. Various Computer-Aided Diagnosis (CADx) schemes have been proposed across modalities, acting as clinical tools that provide a “second opinion” to assist radiologists in the diagnostic task of lesion characterization by means of quantitative image feature extraction and classification methods. The advent of multimodality imaging broadens the role of CADx, in terms of complementary tissue properties analyzed. In this chapter, major stages of CADx schemes in breast imaging are reviewed, while challenges and trends are discussed and highlighted by corresponding application examples of CADx methodologies for microcalcification clusters in mammography and masses in Dynamic Contrast-Enhanced MRI.

Supplemental Screening for Women with Dense Breasts: What Do Practicing Radiologists Recommend?

2019 ◽  
Vol 1 (1) ◽  
pp. 32-36 ◽  
Author(s):  
Tisha Singer ◽  
Ana P Lourenco ◽  
Grayson L Baird ◽  
Martha B Mainiero
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Breast Imaging ◽  
Digital Breast Tomosynthesis ◽  
Risk Groups ◽  
High Rate ◽  
Breast Tomosynthesis ◽  
Dense Breasts ◽  
Additional Imaging ◽  
Magnetic Resonance Imaging Mri

Abstract Objective To evaluate radiologists’ supplemental screening recommendations for women with dense breasts, at average, intermediate, or high risk of breast cancer, and to determine if there are differences between their recommendations for their patients, their friends and family, and themselves. Methods This is an anonymous survey of Society of Breast Imaging (SBI) members. Demographics, knowledge of breast density as a risk factor, and recommendations for screening with digital breast tomosynthesis (DBT), ultrasound (US), and magnetic resonance imaging (MRI) in women with dense breasts, at average, intermediate, or high- risk of breast cancer were assessed. The likelihood of their recommending the screening test for their patients, their family and friends, and themselves was assessed on a Likert scale from 0 to 4 (0 = “not at all likely” to 4 = “extremely likely”). Results There were 295 responses: 67% were women, and breast imaging comprised 95% of their practice. Among participants, 53% correctly answered the question on relative risk of breast cancer when comparing extremely dense versus fatty breasts, and 57% when comparing heterogeneously dense versus scattered breasts. US is recommended at a relatively low rate (1.0–1.4 on the 0–4 scale), regardless of risk. DBT is recommended at a relatively high rate (2.5–3.0 on the 0–4 scale), regardless of risk status. MR is recommended mainly for those at high risk (3.6 on the 0–4 scale). Radiologists were more likely to recommend additional imaging for themselves than for their patients and their family and friends. Conclusion For women with dense breasts, radiologists are “somewhat likely” to recommend US and “likely” to “very likely” to recommend DBT regardless of risk group. They are “very likely” to recommend MRI for high-risk groups.

scholarly journals The potential of utilizing mid-energy X-rays for in-line phase sensitive breast cancer imaging

2020 ◽  
Vol 9 (3-4) ◽  
pp. 89-102
Author(s):  
F.H. Omoumi ◽  
M.U. Ghani ◽  
M.D. Wong ◽  
Y. Li ◽  
B. Zheng ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Phase Contrast ◽  
Low Dose ◽  
Imaging System ◽  
Energy System ◽  
High Energy ◽  
Imaging Systems ◽  
X Rays ◽  
Breast Cancer Imaging ◽  
Phase Sensitive

OBJECTIVE: The objective of this phantom study is to demonstrate the potential of utilizing mid-energy x-rays for in-line phase-sensitive breast cancer imaging by phantom studies. METHODS: The midenergy (50–80 kV) in-line phase sensitive imaging prototype was used to acquire images of the contrast-detail mammography (CDMAM) phantom, an ACR accreditation phantom, and an acrylic edge phantom. The low-dose mid-energy phase-sensitive images were acquired at 60 kV with a radiation dose of 0.9 mGy, while the high-energy phase-sensitive images were acquired at 90 kV with a radiation dose of 1.2 mGy. The Phase-Attenuation Duality (PAD) principle for soft tissue was used for the phase retrieval. A blind observer study was conducted and paired-sample T-test were performed to compare the mean differences in the two imaging systems. RESULTS: The correct detection ratio for the CDMAM phantom for phase-contrast images acquired by the low-dose mid-energy system was 56.91%, whereas images acquired by the high-energy system correctly revealed only 40.97% of discs. The correct detection ratios were 57.88% and 43.41% for phase-retrieved images acquired by the low-dose mid-energy and high-energy imaging systems, respectively. The reading scores for all three groups of objects in the ACR phantom were higher for the mid energy imaging system as compared to the high-energy system for both phase-contrast and phase-retrieved images. The calculated edge enhancement index (EEI) from the acrylic edge phantom image for the mid-energy system was higher than that calculated for the high-energy imaging system. The quantitative analyses showed a higher Contrast to Noise Ratio (CNR) as well as a higher Figure of Merit (FOM) in images acquired by the low-dose mid-energy imaging system. CONCLUSION: The PAD based retrieval method can be applied in mid-energy system without remarkably affecting the image quality, and in fact, it improves the lesion detectability with a patient dose saving of 25%.

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