scholarly journals Identification of Micro-calcification in Mammogram for Breast Cancer Analysis using SVM Classifier

2018 ◽  
Vol 7 (2.16) ◽  
pp. 29
Author(s):  
Gaurav Makwana ◽  
Lalita Gupta
Keyword(s):  
Breast Cancer ◽  
Clustering Algorithm ◽  
Automatic Segmentation ◽  
Breast Cancer Diagnosis ◽  
The State ◽  
Svm Classifier ◽  
Common Disease ◽  
Test Technique ◽  
Novel Approach ◽  
Breast Calcification

Breast cancer is most common disease in women of all ages. To identify & confirm the state of tumor in breast cancer diagnosis, patients are undergo biopsy number of times to identify malignancy. Early detection of cancer can save the patient. In this paper a novel approach for automatic segmentation & classification of breast calcification is proposed. The diagnostic test technique for detection of breast condition is very costly & requires human expertise whereas proposed method can help in automatically identifying the disease by comparing the data with the standard database. In proposed method a database has been created to define various stage of breast calcification & testing images are pre-processed to resize, enhance & filtered to remove background noise. Clustering is performed by using k-means clustering algorithm. GLCM is used to extract out statistical feature like area, mean, variance, standard deviation, homogeneity, skewness etc. to classify the state of tumor. SVM classifier is used for the classification using extracted feature. 

SVM Kernel and Genetic Feature Selection Based Automated Diagnosis of Breast Cancer

2020 ◽  
Vol 13 ◽  
Author(s):  
Indu Singh ◽  
Shashank Garg ◽  
Shivam Arora ◽  
Nikhil Arora ◽  
Kripali Agrawal
Keyword(s):  
Breast Cancer ◽  
Genetic Algorithm ◽  
Feature Selection ◽  
Early Stage ◽  
Feature Space ◽  
Breast Cancer Diagnosis ◽  
Support Vector ◽  
Svm Classifier ◽  
Human Beings ◽  
Novel Approach

Background: Breast cancer is the development of a malignant tumor in the breast of human beings (especially females). If not detected at the initial stages, it can substantially lead to an inoperable construct. It is a reason for majority of cancer-related deaths throughout the world. Objectives: The main aim of our study is to diagnose the breast cancer at early stage so that required treatment can be provided for survival. The tumor is classified as malignant or benign accurately at early stage using a novel approach that includes an ensemble of Genetic Algorithm for feature selection and kernel selection for SVM-Classifier. Methods: The proposed GA-SVM (Genetic Algorithm – Support Vector Machine) algorithm in this paper optimally selects the most appropriate features for training with the SVM classifier. Genetic Programming is used to select the features and the kernel for the SVM classifier. Genetic Algorithm operates by exploring the optimal layout of features for breast cancer, thus, subjugating the problems faced in exponentially immense feature space. Results: The proposed approach accounts for a mean accuracy of 98.82% by using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset available on UCI with the training and testing ratio being 50:50 respectively. Conclusion: The results prove that our proposed model outperforms the previously designed models for breast cancer diagnosis. The outcome assures that the GA-SVM model may be used as an effective tool in assisting the doctors for treating the patients. Alternatively, it may be utilized as an alternate opinion in their eventual diagnosis.

An Improved Strategy for Predicting Diagnosis, Survivability, and Recurrence of Breast Cancer

2019 ◽  
Vol 16 (9) ◽  
pp. 3705-3711 ◽  
Author(s):  
Souad Larabi Marie-Sainte ◽  
Tanzila Saba ◽  
Deem Alsaleh ◽  
Mashael Bin Alamir Alotaibi
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Cancer Diagnosis ◽  
Classification Accuracy ◽  
Cancer Recurrence ◽  
Breast Cancer Diagnosis ◽  
Feedforward Neural Network ◽  
Machine Learning Algorithms ◽  
Common Disease ◽  
Efficient Treatment

Breast Cancer is a common disease among females. Early detection of the Breast Cancer aids in an easier efficient treatment. The application of Machine Learning algorithms can help in the diagnosis of this disease. There are three main problems related to Breast Cancer. The existing works focused only on one problem. In addition, the resulted accuracy still needs improvement. This research paper aims to identify the Breast Cancer diagnosis, predict the recurrence of the disease, and predict the survivability of its patients. This is achieved by using the Feedforward Neural Network (FFN) on the SEER (Surveillance, Epidemiology, and End Results) dataset by using different attributes and preprocessing of data for each problem. The obtained FFN classification accuracy resulted in 99.8% for the Breast Cancer diagnosis, 88.1% for the Breast Cancer recurrence, and 97.3% for the survivability.

A fuzzy based high-resolution multi-view deep CNN for breast cancer diagnosis through SVM classifier on visual analysis

2020 ◽  
Vol 39 (6) ◽  
pp. 8573-8586
Author(s):  
Sudhakar Sengan ◽  
V. Priya ◽  
A. Syed Musthafa ◽  
Logesh Ravi ◽  
Saravanan Palani ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
High Resolution ◽  
Cancer Diagnosis ◽  
Medical Images ◽  
Breast Cancer Diagnosis ◽  
Treatment Decisions ◽  
Diagnosis And Treatment ◽  
Automated System ◽  
Svm Classifier

Breast cancer should be diagnosed as early as possible. A new approach of the diagnosis using deep learning for breast cancer and the particular process using segmentation strategies presented in this article. Medical imagery is an essential tool used for both diagnosis and treatment in many fields of medical applications. But, it takes specially trained medical specialists to read medical images and make diagnoses or treatment decisions. New practices of interpreting medical images are labour exhaustive, time-wasting, expensive, and prone to error. Using a computer-aided program which can render diagnosis and treatment decisions automatically would be more beneficial. A new computer-based detection method for the classification between compassionate and malignant mass tumours in mammography images of the breast proposed. (a) We planned to determine how to use the challenging definition, which produces severe examples that boost the segmentation of mammograms. (b) Employing well designing multi-instance learning through deep learning, we validated employing inadequately labelled data of breast cancer diagnosis using a mammogram. (c) The study is going through the Deep Lung method incorporating deep multi-dimensional automated identification and classification of the lung nodule. (d) By combining a probabilistic graphic model in deep learning, it authorizes how weakly labelled data can be used to improve the existing breast cancer identification method. This automated system involves manually defining the Region Of Interest (ROI), with the region and threshold values based on the next region. The High-Resolution Multi-View Deep Convolutional Neural Network (HRMP-DCNN) mainly developed for the extraction of function. The findings collected through the subsequent in available public databases like mammography screening information database and DDSM Curated Breast Imaging Subset. Ultimately, we’ll show the VGG that’s thousands of times quicker, and it is more reliable than earlier programmed anatomy segmentation.

scholarly journals Automatic segmentation of thermal images to support breast cancer diagnosis

2014 ◽  
Vol 12 (30) ◽  
pp. 25
Author(s):  
Steve Rodríguez Guerrero ◽  
Humberto Loaiza ◽  
Andrés David Restrepo Girón
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Automatic Segmentation ◽  
Breast Cancer Diagnosis ◽  
Thermal Images

scholarly journals Using Support Vector Machine Detection of Breast Cancer in Early stage

2020 ◽  
pp. 213-216
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Early Stage ◽  
Breast Cancer Diagnosis ◽  
Support Vector ◽  
Svm Classifier ◽  
K Nearest Neighbors ◽  
Data Set ◽  
Sensitivity Specificity

The Breast Cancer is disease which tremendously increased in women’s nowadays. Mammography is technique of low-powered X-ray diagnosis approach for detection and diagnosis of cancer diseases at early stage. The proposed system shows the solution of two problems. First shows to detect tumors as suspicious regions with a weak contrast to their background and second shows way to extract features which categorize tumors. Hence this classification can be done with SVM, a great method of statistical learning has made significant achievement in various field. Discovered in the early 90’s, which led to an interest in machine learning? Here the different types of tumor like Benign, Malignant, or Normal image are classified using the SVM classifier. This techniques shows how easily we can detect region of tumor is present in mammogram images with more than 80% of accuracy rates for linear classification using SVM. The 10-fold cross validation to get an accurate outcome is been used by proposed system. The Wisconsin breast cancer diagnosis data set is referred from UCI machine learning repository. The considering accuracy, sensitivity, specificity, false discovery rate, false omission rate and Matthews’s correlation coefficient is appraised in the proposed system. This Provides good result for both training and testing phase. The techniques also shows accuracy of 98.57% and 97.14% by use of Support Vector Machine and K-Nearest Neighbors

scholarly journals Detection and Classification of Breast Cancer in Mammography Images Using Pattern Recognition Methods

2019 ◽  
Vol 3 (4) ◽  
pp. 13-24 ◽  
Author(s):  
Naser Safdarian ◽  
Mohammadreza Hedyezadeh
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Clustering Algorithm ◽  
Probabilistic Neural Network ◽  
Screening Mammography ◽  
Fuzzy Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Final Decision ◽  
Pattern Recognition Methods

Introduction: In this paper, a method is presented to classify the breast cancer masses according to new geometric features. Methods: After obtaining digital breast mammogram images from the digital database for screening mammography (DDSM), image preprocessing was performed. Then, by using image processing methods, an algorithm was developed for automatic extracting of masses from other normal parts of the breast image. In this study, 19 final different features of each image were extracted to generate the feature vector for classifier input. The proposed method not only determined the boundary of masses but also classified the type of masses such as benign and malignant ones. The neural network classification methods such as the radial basis function (RBF), probabilistic neural network (PNN), and multi-layer perceptron (MLP) as well as the Takagi-Sugeno-Kang (TSK) fuzzy classification, the binary statistic classifier, and the k-nearest neighbors (KNN) clustering algorithm were used for the final decision of mass class. Results: The best results of the proposed method for accuracy, sensitivity, and specificity metrics were obtained 97%±4.36, 100%±0 and 96%±5.81, respectively for support vector machine (SVM) classifier. Conclusions: By comparing the results of the proposed method with the results of the other previous methods, the efficiency of the proposed algorithm was reported.

Breast cancer diagnosis based on guided Water Strider Algorithm

2021 ◽  
pp. 095441192110390
Author(s):  
Dezhong Bi ◽  
Yuxi Liu ◽  
Naser Youssefi ◽  
Dan Chen ◽  
Yuexiang Ma
Keyword(s):  
Breast Cancer ◽  
Image Processing ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Final Diagnosis ◽  
Structural Features ◽  
Svm Classifier ◽  
Water Strider ◽  
Early Diagnosis Of Cancer ◽  
Comparison Of The Results

Breast cancer is one of the main cancers that effect of the women’s health. This cancer is one of the most important health issues in the world and because of that, diagnosis in the beginning and appropriate cure is very effective in the recovery and survival of patients, so image processing as a decision-making tool can assist physicians in the early diagnosis of cancer. Image processing mechanisms are simple and non-invasive methods for identifying cancer cells that accelerate early detection and ultimately increase the chances of cancer patients surviving. In this study, a pipeline methodology is proposed for optimal diagnosis of the breast cancer area in the mammography images. Based on the proposed method, after image preprocessing and filtering for noise reduction, a simple and fast tumors mass segmentation based on Otsu threshold segmentation and mathematical morphology is proposed. Afterward, for simplifying the final diagnosis, a feature extraction based on 22 structural features is utilized. To reduce and pruning the useless features, an optimized feature selection based on a new developed design of Water Strider Algorithm (WSA), called Guided WSA (GWSA). Finally, the features injected to an optimized SVM classifier based on GWSA for optimal cancer diagnosis. Simulations of the suggested method are applied to the DDSM database. A comparison of the results with several latest approaches are performed to indicate the method higher effectiveness.

scholarly journals Combining Genetic Algorithms and SVM for Breast Cancer Diagnosis Using Infrared Thermography

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4802
Author(s):  
Roger Resmini ◽  
Lincoln Silva ◽  
Adriel S. Araujo ◽  
Petrucio Medeiros ◽  
Débora Muchaluat-Saade ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Early Diagnosis ◽  
Operating Characteristic ◽  
Cancer Survival ◽  
Characteristic Curve ◽  
Breast Cancer Diagnosis ◽  
Support Vector ◽  
Svm Classifier ◽  
Diagnose Breast Cancer ◽  
Causes Of Mortality

Breast cancer is one of the leading causes of mortality globally, but early diagnosis and treatment can increase the cancer survival rate. In this context, thermography is a suitable approach to help early diagnosis due to the temperature difference between cancerous tissues and healthy neighboring tissues. This work proposes an ensemble method for selecting models and features by combining a Genetic Algorithm (GA) and the Support Vector Machine (SVM) classifier to diagnose breast cancer. Our evaluation demonstrates that the approach presents a significant contribution to the early diagnosis of breast cancer, presenting results with 94.79% Area Under the Receiver Operating Characteristic Curve and 97.18% of Accuracy.

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