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.

scholarly journals Improving the performance of support-vector machine by selecting the best features by Gray Wolf algorithm to increase the accuracy of diagnosis of breast cancer

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Seyed Reza Kamel ◽  
Reyhaneh YaghoubZadeh ◽  
Maryam Kheirabadi
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Support Vector Machine ◽  
Feature Selection Method ◽  
Breast Cancer Diagnosis ◽  
Support Vector ◽  
Gray Wolf ◽  
Diagnose Breast Cancer ◽  
Computer Methods ◽  
A New Technique

Abstract One of the most common diseases among women is breast cancer, the early diagnosis of which is of paramount importance. Given the time-consuming nature of the diagnosis process of the disease, using new methods such as computer science is extremely important for early detection of the condition. Today, the main emphasis is on the science of data mining as one of the computer methods in the field of diagnosis. In the present study, we used data mining as a combination of feature selection method by Gray Wolf Optimization (GWO) and support vector machine (SVM), which is a new technique with high accuracy compared to other methods in this classification, to increase the accuracy of breast cancer diagnosis. The UCI dataset and functional parameters and various statistical criteria were applied to evaluate the proposed method and assess the validity of the results in MATLAB, respectively. Application of the proposed method increased the improvement of the evaluated criteria, which increased the accuracy of diagnosis by 27.68%, compared to former works in the field. As such, it could be concluded that the proposed method had a higher ability to diagnose breast cancer, compared to previous techniques.

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 Breast Cancer Diagnosis (BCD) Model Using Machine Learning

2019 ◽  
Vol 8 (10) ◽  
pp. 4456-4463
Keyword(s):  
Breast Cancer ◽  
Early Stage ◽  
Principal Component ◽  
Feature Space ◽  
Breast Cancer Diagnosis ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Calibration Plot ◽  
K Nearest Neighbors ◽  
Diagnose Breast Cancer

In the recent years, breast cancer research has made a significant growth however there is still a scope of advancement. Breast cancer increases the statistics of mortality among women. In concern to this issue, treatment of cancer should be started at the earlier stage, to increase the chances of survival of the patient. Thus, there is a need to diagnose breast cancer at the early stage using the features from the mammograms. This paper proposes an efficient BCD model to detect breast cancer by using Support Vector Machine (SVM) with 10-fold cross validation. The complexity of the problem increases if there are many input features for the diagnosis of cancer. Thus, Principal Component Analysis (PCA) is used to reduce the feature space from a higher dimension to a lower dimension. Experiment result shows that the PCA increases the accuracy of the model. The proposed BCD model is compared with other supervised learning algorithms like Decision trees (DT), Random Forest, k- Nearest Neighbors(k-NN), Stochastic Gradient Descent (SGD), AdaBoost, Neural Network (NN), and Naïve Bayes. Evaluation parameters like F1 measure, ROC curve, Accuracy, Lift curve and Calibration Plot proves that proposed BCD model outperforms and gives the highest accuracy among other compared algorithms

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.

scholarly journals ENHANCED SVM CLASSIFIER FOR BREAST CANCER DIAGNOSIS

2020 ◽  
Vol 5 (3) ◽  
pp. 67-74 ◽  
Author(s):  
M. Kavitha ◽  
G. Lavanya ◽  
J. Janani ◽  
Balaji. J
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Classification Performance ◽  
Supervised Machine Learning ◽  
Basis Set ◽  
Support Vector ◽  
Svm Classifier ◽  
Breast Image

Breast cancer is the leading disease to cause death especially in women. In this paper, a frame work based algorithm for the classification of cancerous/non-cancerous data is developed using application of supervised machine learning. In feature selection, we derive basis set in the kernel space and then we extend the margin based feature selection algorithm. We are trying to explore several feature selection, extraction techniques and combine the optimal feature subsets with various learning classification methods such as KNN, PNN and Support Vector Machine (SVM) classifiers. The best classification performance for breast cancer diagnosis is attained equal to 99.17% between radius and compact features using SVM classifier. And also derive the features of a breast image in the WBCD dataset.

scholarly journals Advanced and futuristic approaches for breast cancer diagnosis

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Jayanti Mishra ◽  
Bhumika Kumar ◽  
Monika Targhotra ◽  
P. K. Sahoo
Keyword(s):  
Breast Cancer ◽  
Early Diagnosis ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Diagnostic Techniques ◽  
Main Body ◽  
Detection Techniques ◽  
Initial Stage ◽  
Common Causes ◽  
Delay In Treatment

Abstract Background Breast cancer is the most frequent cancer and one of the most common causes of death in women, impacting almost 2 million women each year. Tenacity or perseverance of breast cancer in women is very high these days with an extensive increasing rate of 3 to 5% every year. Along with hurdles faced during treatment of breast tumor, one of the crucial causes of delay in treatment is invasive and poor diagnostic techniques for breast cancer hence the early diagnosis of breast tumors will help us to improve its management and treatment in the initial stage. Main body Present review aims to explore diagnostic techniques for breast cancer that are currently being used, recent advancements that aids in prior detection and evaluation and are extensively focused on techniques that are going to be future of breast cancer detection with better efficiency and lesser pain to patients so that it helps to a physician to prevent delay in treatment of cancer. Here, we have discussed mammography and its advanced forms that are the need of current era, techniques involving radiation such as radionuclide methods, the potential of nanotechnology by using nanoparticle in breast cancer, and how the new inventions such as breath biopsy, and X-ray diffraction of hair can simply use as a prominent method in breast cancer early and easy detection tool. Conclusion It is observed significantly that advancement in detection techniques is helping in early diagnosis of breast cancer; however, we have to also focus on techniques that will improve the future of cancer diagnosis in like optical imaging and HER2 testing.

scholarly journals Predicting Risk of Antenatal Depression and Anxiety Using Multi-Layer Perceptrons and Support Vector Machines

2021 ◽  
Vol 11 (3) ◽  
pp. 199
Author(s):  
Fajar Javed ◽  
Syed Omer Gilani ◽  
Seemab Latif ◽  
Asim Waris ◽  
Mohsin Jamil ◽  
...  
Keyword(s):  
Low Income ◽  
Operating Characteristic ◽  
Mental Health Problems ◽  
Characteristic Curve ◽  
Antenatal Depression ◽  
Low Income Countries ◽  
Support Vector ◽  
Depression And Anxiety ◽  
Gynecology And Obstetrics ◽  
Operating Characteristic Curve

Perinatal depression and anxiety are defined to be the mental health problems a woman faces during pregnancy, around childbirth, and after child delivery. While this often occurs in women and affects all family members including the infant, it can easily go undetected and underdiagnosed. The prevalence rates of antenatal depression and anxiety worldwide, especially in low-income countries, are extremely high. The wide majority suffers from mild to moderate depression with the risk of leading to impaired child–mother relationship and infant health, few women end up taking their own lives. Owing to high costs and non-availability of resources, it is almost impossible to diagnose every pregnant woman for depression/anxiety whereas under-detection can have a lasting impact on mother and child’s health. This work proposes a multi-layer perceptron based neural network (MLP-NN) classifier to predict the risk of depression and anxiety in pregnant women. We trained and evaluated our proposed system on a Pakistani dataset of 500 women in their antenatal period. ReliefF was used for feature selection before classifier training. Evaluation metrics such as accuracy, sensitivity, specificity, precision, F1 score, and area under the receiver operating characteristic curve were used to evaluate the performance of the trained model. Multilayer perceptron and support vector classifier achieved an area under the receiving operating characteristic curve of 88% and 80% for antenatal depression and 85% and 77% for antenatal anxiety, respectively. The system can be used as a facilitator for screening women during their routine visits in the hospital’s gynecology and obstetrics departments.

scholarly journals Design of an SVM Classifier Assisted Intelligent Receiver for Reliable Optical Camera Communication

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4283
Author(s):  
Md.-Habibur Rahman ◽  
Md. Shahjalal ◽  
Moh. Khalid Hasan ◽  
Md.-Osman Ali ◽  
Yeong-Min Jang
Keyword(s):  
Light Emitting Diode ◽  
Characteristic Curve ◽  
Analysis Data ◽  
Image Sensor ◽  
Data Rate ◽  
Support Vector ◽  
Svm Classifier ◽  
High Data ◽  
Receiver System ◽  
Optical Camera Communication

Embedding optical camera communication (OCC) commercially as a favorable complement of radio-frequency technology has led to the desire for an intelligent receiver system that is eligible to communicate with an accurate light-emitting diode (LED) transmitter. To shed light on this issue, a novel scheme for detecting and recognizing data transmitting LEDs has been elucidated in this paper. Since the optically modulated signal is captured wirelessly by a camera that plays the role of the receiver for the OCC technology, the process to detect LED region and retrieval of exact information from the image sensor is required to be intelligent enough to achieve a low bit error rate (BER) and high data rate to ensure reliable optical communication within limited computational abilities of the most used commercial cameras such as those in smartphones, vehicles, and mobile robots. In the proposed scheme, we have designed an intelligent camera receiver system that is capable of separating accurate data transmitting LED regions removing other unwanted LED regions employing a support vector machine (SVM) classifier along with a convolutional neural network (CNN) in the camera receiver. CNN is used to detect every LED region from the image frame and then essential features are extracted to feed into an SVM classifier for further accurate classification. The receiver operating characteristic curve and other key performance parameters of the classifier have been analyzed broadly to evaluate the performance, justify the assistance of the SVM classifier in recognizing the accurate LED region, and decode data with low BER. To investigate communication performances, BER analysis, data rate, and inter-symbol interference have been elaborately demonstrated for the proposed intelligent receiver. In addition, BER against distance and BER against data rate have also been exhibited to validate the effectiveness of our proposed scheme comparing with only CNN and only SVM classifier based receivers individually. Experimental results have ensured the robustness and applicability of the proposed scheme both in the static and mobile scenarios.

Protein biomarkers in breast cancer-derived extracellular vesicles for use in liquid biopsies

2021 ◽  
Author(s):  
Dan Li ◽  
Wenjia Lai ◽  
Di Fan ◽  
Qiaojun Fang
Keyword(s):  
Breast Cancer ◽  
Early Diagnosis ◽  
Extracellular Vesicles ◽  
Liquid Biopsy ◽  
Breast Cancer Diagnosis ◽  
Extracellular Vesicle ◽  
Detection Methods ◽  
Protein Markers ◽  
Liquid Biopsies ◽  
Diagnosis And Prognosis

Breast cancer is the most common malignant disease in women worldwide. Early diagnosis and treatment can greatly improve the management of breast cancer. Liquid biopsies are becoming convenient detection methods for diagnosing and monitoring breast cancer due to their non-invasiveness and ability to provide real-time feedback. A range of liquid biopsy markers, including circulating tumor proteins, circulating tumor cells, and circulating tumor nucleic acids, have been implemented for breast cancer diagnosis and prognosis, with each having its own advantages and limitations. Circulating extracellular vesicles are messengers of intercellular communication that are packed with information from mother cells and are found in a wide variety of bodily fluids; thus, they are emerging as ideal candidates for liquid biopsy biomarkers. In this review, we summarize extracellular vesicle protein markers that can be potentially used for the early diagnosis and prognosis of breast cancer or determining its specific subtypes.

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