Breast cancer analysis using Machine Learning algorithms

2021 ◽  
Vol 2 (1) ◽  
pp. 12-16
Author(s):  
Lina Alkhathlan ◽  
Abdul Khader Jilani Saudagar
Keyword(s):  
Breast Cancer ◽  
Breast Tissue ◽  
Causes Of Death ◽  
State Of The Art ◽  
Early Stage ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Risk Of Death ◽  
Breast Cells

Breast cancer (BC) is one of the most common types of cancer and one of the leading causes of death for women around the world. Breast cancer occurs when cells in the breast cells mutate and form a malignant tumor. State-of-the-art technologies can detect BC at an early stage, which helps in treatment and reduces the risk of death. Medical doctors commonly use breast tissue biopsy when diagnosing breast cancer, enabling them to take a microscopic look for breast tissue and determine whether the tissue is benign or malignant. To improve biopsy results, many researchers have studied the feasibility of using artificial intelligence (AI) to help doctors detect any harmful changes that may lead to cancer. In this research work detail analysis of Breast Cancer using support vector machine (SVM) and convolutional neural networks (CNN) algorithms is performed and the results show CNN has more superior results in comparison to SVM in the recognition of images affected by Breast Cancer.

scholarly journals AN IN-DEPTH ANALYSIS OF THE IDENTIFIED ALGORITHMS AND THEIR COMPARATIVE STUDY IN THE EARLY DETECTION AND DIAGNOSIS OF BREAST CANCER

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Mridul Sharma
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Early Detection ◽  
Healthcare Workers ◽  
State Of The Art ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Depth Analysis ◽  
Detection And Diagnosis ◽  
Early Detection And Diagnosis

These days one of the major inevitable ailments for females is bosom malignancy. The appropriate medication and early findings are important stages to take to thwart this ailment. Although, it's not easy to recognize due to its few vulnerabilities and lack of data. Can use artificial intelligence to create devices that can help doctors and healthcare workers to early detection of this cancer. In This research, we investigate three specific machine learning algorithms widely used to detect bosom ailments in the breast region. These algorithms are Support vector machine (SVM), Bayesian Networks (BN) and Random Forest (RF). The output in this research is based on the State-of-the-art technique.

scholarly journals Comparison of Classification Models for Breast Cancer Identification using Google Colab

2020 ◽  
Author(s):  
SUNDARAMBAL BALARAMAN
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Logistic Regression ◽  
Learning Algorithms ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Classification Algorithms ◽  
Classification Models ◽  
K Nearest Neighbor

Classification algorithms are very widely used algorithms for the study of various categories of data located in multiple databases that have real-world implementations. The main purpose of this research work is to identify the efficiency of classification algorithms in the study of breast cancer analysis. Mortality rate of women increases due to frequent cases of breast cancer. The conventional method of diagnosing breast cancer is time consuming and hence research works are being carried out in multiple dimensions to address this issue. In this research work, Google colab, an excellent environment for Python coders, is used as a tool to implement machine learning algorithms for predicting the type of cancer. The performance of machine learning algorithms is analyzed based on the accuracy obtained from various classification models such as logistic regression, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Naïve Bayes, Decision Tree and Random forest. Experiments show that these classifiers work well for the classification of breast cancers with accuracy>90% and the logistic regression stood top with an accuracy of 98.5%. Also implementation using Google colab made the task very easier without spending hours of installation of environment and supporting libraries which we used to do earlier.

Synthesizing Conjunctive & Disjunctive Linear Invariants by K-means++ and SVM

2020 ◽  
Vol 17 (6) ◽  
pp. 847-856
Author(s):  
Shengbing Ren ◽  
Xiang Zhang
Keyword(s):  
Software Verification ◽  
State Of The Art ◽  
Positive Sample ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Hoare Logic ◽  
Excellent Performance ◽  
Automated Software ◽  
Inductive Invariants ◽  
Linear Invariants

The problem of synthesizing adequate inductive invariants lies at the heart of automated software verification. The state-of-the-art machine learning algorithms for synthesizing invariants have gradually shown its excellent performance. However, synthesizing disjunctive invariants is a difficult task. In this paper, we propose a method k++ Support Vector Machine (SVM) integrating k-means++ and SVM to synthesize conjunctive and disjunctive invariants. At first, given a program, we start with executing the program to collect program states. Next, k++SVM adopts k-means++ to cluster the positive samples and then applies SVM to distinguish each positive sample cluster from all negative samples to synthesize the candidate invariants. Finally, a set of theories founded on Hoare logic are adopted to check whether the candidate invariants are true invariants. If the candidate invariants fail the check, we should sample more states and repeat our algorithm. The experimental results show that k++SVM is compatible with the algorithms for Intersection Of Half-space (IOH) and more efficient than the tool of Interproc. Furthermore, it is shown that our method can synthesize conjunctive and disjunctive invariants automatically

scholarly journals De-escalation of radiation therapy in patients with stage I, node-negative, HER2-positive breast cancer

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Jose G. Bazan ◽  
Sachin R. Jhawar ◽  
Daniel Stover ◽  
Ko Un Park ◽  
Sasha Beyer ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Early Stage ◽  
Local Therapy ◽  
Adjuvant Radiation ◽  
Her2 Positive ◽  
Node Negative ◽  
Risk Of Death ◽  
Her2 Breast Cancer ◽  
Neoadjuvant Systemic Therapy ◽  
Increased Risk

AbstractIn the modern era, highly effective anti-HER2 therapy is associated with low local-regional recurrence (LRR) rates for early-stage HER2+ breast cancer raising the question of whether local therapy de-escalation by radiation omission is possible in patients with small-node negative tumors treated with lumpectomy. To evaluate existing data on radiation omission, we used the National Cancer Database (NCDB) to test the hypothesis that RT omission results in equivalent overall survival (OS) in stage 1 (T1N0) HER2+ breast cancer. We excluded patients that received neoadjuvant systemic therapy. We stratified the cohort by receipt of adjuvant radiation. We identified 6897 patients (6388 RT; 509 no RT). Patients that did not receive radiation tended to be ≥70 years-old (odds ratio [OR] = 3.69, 95% CI: 3.02–4.51, p < 0.0001), to have ≥1 comorbidity (OR = 1.33, 95% CI: 1.06–1.68, p = 0.0154), to be Hispanic (OR = 1.49, 95% CI: 1.00–2.22, p = 0.049), and to live in lower income areas (OR = 1.32, 95% CI: 1.07–1.64, p = 0.0266). Radiation omission was associated with a 3.67-fold (95% CI: 2.23–6.02, p < 0.0001) increased risk of death. While other selection biases that influence radiation omission likely persist, these data should give caution to radiation omission in T1N0 HER2+ breast cancer.

A Comparative Study of Alzheimer Detection Techniques

2021 ◽  
Vol 9 (8) ◽  
pp. 2877-2883
Author(s):  
Adwait Patil
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Comparative Study ◽  
Fuzzy System ◽  
Early Stage ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Detection Techniques

Abstract: Alzheimer’s disease is one of the neurodegenerative disorders. It initially starts with innocuous symptoms but gradually becomes severe. This disease is so dangerous because there is no treatment, the disease is detected but typically at a later stage. So it is important to detect Alzheimer at an early stage to counter the disease and for a probable recovery for the patient. There are various approaches currently used to detect symptoms of Alzheimer’s disease (AD) at an early stage. The fuzzy system approach is not widely used as it heavily depends on expert knowledge but is quite efficient in detecting AD as it provides a mathematical foundation for interpreting the human cognitive processes. Another more accurate and widely accepted approach is the machine learning detection of AD stages which uses machine learning algorithms like Support Vector Machines (SVMs) , Decision Tree , Random Forests to detect the stage depending on the data provided. The final approach is the Deep Learning approach using multi-modal data that combines image , genetic data and patient data using deep models and then uses the concatenated data to detect the AD stage more efficiently; this method is obscure as it requires huge volumes of data. This paper elaborates on all the three approaches and provides a comparative study about them and which method is more efficient for AD detection. Keywords: Alzheimer’s Disease (AD), Fuzzy System , Machine Learning , Deep Learning , Multimodal data

AUTOMATIC SEGMENTATION OF BREAST TISSUE THERMAL IMAGES

2018 ◽  
Vol 30 (03) ◽  
pp. 1850024 ◽  
Author(s):  
Zeinab Heidari ◽  
Mehrdad Dadgostar ◽  
Zahra Einalou
Keyword(s):  
Breast Cancer ◽  
Image Processing ◽  
Breast Imaging ◽  
Breast Tissue ◽  
Early Stage ◽  
Automatic Segmentation ◽  
Image Data ◽  
Thermal Images ◽  
Early Stages ◽  
Invasive Method

Breast cancer is one of the main causes of women’s death. Thermal breast imaging is one the non-invasive method for cancer at early stage diagnosis. In contrast to mammography this method is cheap and painless and it can be used during pregnancy while ionized beams are not used. Specialists are seeking new ways to diagnose the cancer in early stages. Segmentation of the breast tissue is one of the most indispensable stages in most of the cancer diagnosis methods. By the advancement of infrared precise cameras, new and fast computers and nouvelle image processing approaches, it is feasible to use thermal imaging for diagnosis of breast cancer at early stages. Since the breast form is different in individuals, image segmentation is a hard task and semi-automatic or manual methods are usual in investigations. In this research the image data base of DMR-IR has been utilized and a now automatic approach has been proposed which does not need learning. Data were included 159 gray images used by dynamic protocol (132 healthy and 27 patients). In this study, by combination of different image processing methods, the segmentation of thermal images of the breast tissues have been completed automatically and results show the proper performance of recommended method.

scholarly journals Breast Thermograms Asymmetry Analysis using Gabor filters

2021 ◽  
Vol 309 ◽  
pp. 01109
Author(s):  
Priyanka Yadlapalli ◽  
Madhavi K Reddy ◽  
Sunitha Gurram ◽  
J Avanija ◽  
K Meenakshi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Early Stage ◽  
Cost Effective ◽  
Gabor Filters ◽  
Support Vector ◽  
Svm Classifier ◽  
Infrared Thermal Imaging ◽  
Younger Women ◽  
Early Diagnosis Of Cancer ◽  
Diagnosis Of Cancer

Women are far more likely than males to acquire breast cancer, and current research indicates that this is entirely avoidable. It is also to blame for higher death rates among younger women compared to older women in nearly all developing nations. Medical imaging modalities are continuously in need of development. A variety of medical techniques have been employed to detect breast cancer in women. The most recent studies support mammography for breast cancer screening, although its sensitivity and specificity remain suboptimal, particularly in individuals with thick breast tissue, such as young women. As a result, alternative modalities, such as thermography, are required. Digital Infrared Thermal Imaging (DITI), as it is known, detects and records temperature changes on the skin’s surface. Thermography is well-known for its non-invasive, painless, cost-effective, and high recovery rates, as well as its potential to identify breast cancer at an early stage. Gabor filters are used to extract the textural characteristics of the left and right breasts. Using a support vector machine, the thermograms are then classified as normal or malignant based on textural asymmetry between the breasts (SVM). The accuracy achieved by combining Gabor features with an SVM classifier is around 84.5 percent. The early diagnosis of cancer with thermography enhances the patient’s chances of survival significantly since it may detect the disease in its early stages.

Effect of Radiotherapy After Breast-Conserving Treatment in Women With Breast Cancer and Germline BRCA1/2 Mutations

2000 ◽  
Vol 18 (19) ◽  
pp. 3360-3369 ◽  
Author(s):  
Lori J. Pierce ◽  
Myla Strawderman ◽  
Steven A. Narod ◽  
Ivo Oliviotto ◽  
Andrea Eisen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cellular Response ◽  
Breast Tissue ◽  
Early Stage ◽  
Subcutaneous Tissue ◽  
Conditional Logistic Regression ◽  
Radiation Therapy Oncology Group ◽  
Laboratory Data ◽  
Tumor Control ◽  
European Organization

PURPOSE: Recent laboratory data suggest a role for BRCA1/2 in the cellular response to DNA damage. There is a paucity of clinical data, however, examining the effect of radiotherapy (RT), which causes double-strand breaks, on breast tissue from BRCA1/2 mutation carriers. Thus the goals of this study were to compare rates of radiation-associated complications, in-breast tumor recurrence, and distant relapse in women with BRCA1/2 mutations treated with breast-conserving therapy (BCT) using RT with rates observed in sporadic disease. PATIENTS AND METHODS: Seventy-one women with a BRCA1/2 mutation and stage I or II breast cancer treated with BCT were matched 1:3 with 213 women with sporadic breast cancer. Conditional logistic regression models were used to compare matched cohorts for rates of complications and recurrence. RESULTS: Tumors from women in the genetic cohort were associated with high histologic (P = .0004) and nuclear (P = .009) grade and negative estrogen (P = .0001) and progesterone (P = .002) receptors compared with tumors from the sporadic cohort. Using Radiation Therapy Oncology Group/European Organization for Research and Treatment of Cancer toxicity scoring, there were no significant differences in acute or chronic morbidity in skin, subcutaneous tissue, lung, or bone. The 5-year actuarial overall survival, relapse-free survival, and rates of tumor control in the treated breast for the patients in the genetic cohort were 86%, 78%, and 98%, respectively, compared with 91%, 80%, and 96%, respectively, for the sporadic cohort (P = not significant). CONCLUSION: There was no evidence of increased radiation sensitivity or sequelae in breast tissue heterozygous for a BRCA1/2 germline mutation compared with controls, and rates of tumor control in the breast and survival were comparable between BRCA1/2 carriers and controls at 5 years. Although additional follow-up is needed, these data may help in discussing treatment options in the management of early-stage hereditary breast cancer and should provide reassurance regarding the safety of administering RT to carriers of a germline BRCA1/2 mutation.

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.

Survey of Breast Cancer Detection Using Machine Learning Techniques in Big Data

2019 ◽  
Vol 21 (3) ◽  
pp. 80-92
Author(s):  
Madhuri Gupta ◽  
Bharat Gupta
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Research Work ◽  
Cancer Recurrence ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Common Disease ◽  
Learning Techniques ◽  
Learning Frameworks ◽  
Processing Engine

Cancer is a disease in which cells in body grow and divide beyond the control. Breast cancer is the second most common disease after lung cancer in women. Incredible advances in health sciences and biotechnology have prompted a huge amount of gene expression and clinical data. Machine learning techniques are improving the prior detection of breast cancer from this data. The research work carried out focuses on the application of machine learning methods, data analytic techniques, tools, and frameworks in the field of breast cancer research with respect to cancer survivability, cancer recurrence, cancer prediction and detection. Some of the widely used machine learning techniques used for detection of breast cancer are support vector machine and artificial neural network. Apache Spark data processing engine is found to be compatible with most of the machine learning frameworks.

Sign in / Sign up

Export Citation Format

Share Document