scholarly journals Breast Cancer Diagnosis using Machine Learning Approach

2021 ◽  
pp. 459-466
Author(s):  
Nanchen Nimyel Caleb ◽  
Selfa Johnson Zwalnan ◽  
Cornelius A. Pahalson
Keyword(s):  
Breast Cancer ◽  
Random Forest ◽  
Early Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Breast Disease ◽  
Random Forest Classifier ◽  
Breast Diseases ◽  
K Nearest Neighbors ◽  
Cancer Dataset ◽  
Research Areas

Breast cancer is the second most common cancer in women after skin cancer. When cancer care is delayed or inaccessible, there is a lower chance of survival, greater problems associated with treatment and higher costs of care. Early diagnosis improves cancer outcomes and leads to a better prognosis. In third world countries like Nigeria, where state-of-the art breast cancer diagnostic machines and the experts are grossly insufficient, alternative approaches to early diagnosis of breast cancer must be evolved. These preliminary data obtained from images of suspected cases of breast cancer are transformed in profiles of breast diseases, which are used by the local physicians in charge of breast disease patients. Each new case can then be compared by the local treating physician with the profile of all preceded cases with the same diagnosis. Three supervised learning models; Logistic Regression. Random Forest Classifier, and K-Nearest Neighbors were used to train the cancer dataset, and Random Forest Classifier outperformed with accuracy of 96% and an almost perfect sensitivity/Recall index. The dataset could not capture the demographic effects of the breast cancer images on the diagnosis, which now opens up new research areas in this study of breast cancer.

scholarly journals Classifications of Breast Cancer Diagnosis using Machine Learning

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Random Forest ◽  
Breast Cancer Diagnosis ◽  
Performance Comparison ◽  
Support Vector ◽  
Breast Cancer Dataset ◽  
K Nearest Neighbors ◽  
Cancer Dataset ◽  
Machine Learning Classification

Breast Cancer (BC) is amongst the most common and leading causes of deaths in women throughout the world. Recently, classification and data analysis tools are being widely used in the medical field for diagnosis, prognosis and decision making to help lower down the risks of people dying or suffering from diseases. Advanced machine learning methods have proven to give hope for patients as this has helped the doctors in early detection of diseases like Breast Cancer that can be fatal, in support with providing accurate outcomes. However, the results highly depend on the techniques used for feature selection and classification which will produce a strong machine learning model. In this paper, a performance comparison is conducted using four classifiers which are Multilayer Perceptron (MLP), Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Random Forest on the Wisconsin Breast Cancer dataset to spot the most effective predictors. The main goal is to apply best machine learning classification methods to predict the Breast Cancer as benign or malignant using terms such as accuracy, f-measure, precision and recall. Experimental results show that Random forest is proven to achieve the highest accuracy of 99.26% on this dataset and features, while SVM and KNN show 97.78% and 97.04% accuracy respectively. MLP shows the least accuracy of 94.07%. All the experiments are conducted using RStudio as the data mining tool platform.

Fuzzy Logic for Breast Cancer Diagnosis Using Medical Thermogram Images

2017 ◽  
pp. 354-388 ◽  
Author(s):  
Surekha Kamath
Keyword(s):  
Breast Cancer ◽  
Fuzzy Logic ◽  
Early Detection ◽  
Early Diagnosis ◽  
Cause Of Death ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Detection Technique ◽  
Breast Diseases ◽  
Benign Neoplasms

In this chapter, how medical thermography can be utilized as early detection technique for breast cancer with fuzzy logic is explained. Breast cancer is the leading cause of death among women. This fact justifies researches to reach early diagnosis, improving patients' life expectancies. Moreover, there are other pathologies, such as cysts and benign neoplasms, that deserve investigation. In the last ten years, the infrared thermography has shown to be a promising technique to early diagnosis of breast pathologies. Works on this subject presented results that justify the thermography as a complementary exam to detect breast diseases. Various algorithms that can be utilized for Breast Cancer diagnosis utilizing medical thermography are listed and also the advantages of medical thermography over other imaging modalities is given.

scholarly journals Use of Autoantibodies to Detect the Onset of Breast Cancer

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Jérôme Lacombe ◽  
Alain Mangé ◽  
Jérôme Solassol
Keyword(s):  
Breast Cancer ◽  
Early Diagnosis ◽  
Early Stage ◽  
Tumor Development ◽  
Breast Cancer Diagnosis ◽  
Breast Disease ◽  
Screening Mammography ◽  
Early Stage Breast Cancer ◽  
Highly Sensitive Detection

The widespread use of screening mammography has resulted in increased detection of early-stage breast disease, particularly forin situcarcinoma and early-stage breast cancer. However, the majority of women with abnormalities noted on screening mammograms are not diagnosed with cancer because of several factors, including radiologist assessment, patient age, breast density, malpractice concerns, and quality control procedures. Although magnetic resonance imaging is a highly sensitive detection tool that has become standard for women at very high risk of developing breast cancer, it lacks sufficient specificity and costeffectiveness for use as a general screening tool. Therefore, there is an important need to improve screening and diagnosis of early-invasive and noninvasive tumors, that is,in situcarcinoma. The great potential for molecular tools to improve breast cancer outcomes based on early diagnosis has driven the search for diagnostic biomarkers. Identification of tumor-specific markers capable of eliciting an immune response in the early stages of tumor development seems to provide an effective approach for early diagnosis. The aim of this review is to describe several autoantibodies identified during breast cancer diagnosis. We will focus on these molecules highlighted in the past two years and discuss the potential future use of autoantibodies as biomarkers of early-stage breast cancer.

Breast Cancer Detection Using Random Forest Classifier

2021 ◽  
pp. 85-98
Author(s):  
Pavithra Suchindran ◽  
Vanithamani R. ◽  
Judith Justin
Keyword(s):  
Breast Cancer ◽  
Random Forest ◽  
Cancer Detection ◽  
Region Of Interest ◽  
Texture Features ◽  
Speckle Noise ◽  
Random Forest Classifier ◽  
Breast Cancer Detection ◽  
K Nearest Neighbors ◽  
Cad System

Breast cancer is the second most prevalent type of cancer among women. Breast ultrasound (BUS) imaging is one of the most frequently used diagnostic tools to detect and classify abnormalities in the breast. To improve the diagnostic accuracy, computer-aided diagnosis (CAD) system is helpful for breast cancer detection and classification. Normally, a CAD system consists of four stages: pre-processing, segmentation, feature extraction, and classification. In this chapter, the pre-processing step includes speckle noise removal using speckle reducing anisotropic diffusion (SRAD) filter. The goal of segmentation is to locate the region of interest (ROI) and active contour-based segmentation and fuzzy C means segmentation (FCM) are used in this work. The texture features are extracted and fed to a classifier to categorize the images as normal, benign, and malignant. In this work, three classifiers, namely k-nearest neighbors (KNN) algorithm, decision tree algorithm, and random forest classifier, are used and the performance is compared based on the accuracy of classification.

scholarly journals Early Diagnosis of Breast Cancer Prediction using Random Forest Classifier

2021 ◽  
Vol 1116 (1) ◽  
pp. 012187
Author(s):  
P R Anisha ◽  
C Kishor Kumar Reddy ◽  
K Apoorva ◽  
C Meghana Mangipudi
Keyword(s):  
Breast Cancer ◽  
Random Forest ◽  
Early Diagnosis ◽  
Random Forest Classifier ◽  
Cancer Prediction

scholarly journals RFSVM: A Novel Classification Technique for Breast Cancer Diagnosis

2019 ◽  
Vol 8 (12) ◽  
pp. 3295-3305
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Random Forest ◽  
Early Stage ◽  
Breast Cancer Diagnosis ◽  
Support Vector ◽  
Breast Cancer Dataset ◽  
Limited Data ◽  
Cancer Dataset ◽  
Cancerous Cell

Cancer is a disease, which develops, in human body due to gene mutation. Due to various factor cells turn into cancerous cell and grow rapidly while damaging normal cells. Many women get affected by breast cancer, which might even cause death if not treated at early stage. Early detection of breast cancer is highly important to increase the survival rate. Machine learning methods and technologies are making it possible to classify and detect the class in an accurate manner. Among other classifiers, random forest and support vector machine are two classifiers that have a good classification power. In this, research a combination of these two classifier i.e. Random Forest and Support Vector Machine (RFSVM) is proposed for early diagnosis of breast cancer cell using Wisconsin Breast Cancer Dataset (WBCD). Using different train-test data ratio experiments are performed and an average of more than 98percentage accuracy is achieved using this hybrid classifier. This paper overcomes the over-fitting problem of random forest and the need of tuning the parameters of Support Vector Machine. Even with limited data available, the classifier tunes its parameters so well to give a highly accurate result.

Fuzzy Logic for Breast Cancer Diagnosis Using Medical Thermogram Images

2015 ◽  
pp. 168-199
Author(s):  
Surekha Kamath
Keyword(s):  
Breast Cancer ◽  
Fuzzy Logic ◽  
Early Detection ◽  
Early Diagnosis ◽  
Cause Of Death ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Detection Technique ◽  
Breast Diseases ◽  
Benign Neoplasms

In this chapter, how medical thermography can be utilized as early detection technique for breast cancer with fuzzy logic is explained. Breast cancer is the leading cause of death among women. This fact justifies researches to reach early diagnosis, improving patients' life expectancies. Moreover, there are other pathologies, such as cysts and benign neoplasms, that deserve investigation. In the last ten years, the infrared thermography has shown to be a promising technique to early diagnosis of breast pathologies. Works on this subject presented results that justify the thermography as a complementary exam to detect breast diseases. Various algorithms that can be utilized for Breast Cancer diagnosis utilizing medical thermography are listed and also the advantages of medical thermography over other imaging modalities is given.

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.

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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