Breast Cancer Diagnosis Using Machine Learning Algorithms - A Survey

Abstract

Breast cancer has become a common factor now-a-days. Despite the fact, not all general hospitalshave the facilities to diagnose breast cancer through mammograms. Waiting for diagnosing a breastcancer for a long time may increase the possibility of the cancer spreading. Therefore a computerizedbreast cancer diagnosis has been developed to reduce the time taken to diagnose the breast cancer andreduce the death rate. This paper summarizes the survey on breast cancer diagnosis using various machinelearning algorithms and methods, which are used to improve the accuracy of predicting cancer. This surveycan also help us to know about number of papers that are implemented to diagnose the breast cancer

Using Internet of Things (IoT) Data in Reducing Cancer Risk in Women

“…[16,17] These noisy pixels are replaced by the average of neighbouring pixels that pass the noise encoding test. [18] These images show the difference between malignant and benign breast tissue.…”

Recent Advances on Mammogram Imaging for Breast Cancer Analysis: A Technological Review

“…Data mining algorithms such as Naïve Bayes classifier, Decision trees CART & J48, Radial Basis Neural Networks and SVM implemented by Aruna et al to detect 2lesions in breast [20]. Gayathri et al [22] investigated three algorithms REPTree, Simple Logistic and RBF Network for survival rate prediction in patients with breast cancer. They used a workbench called Weka to implement all these classification techniques on a breast cancer dataset.…”

Survey of Machine Learning Techniques in Medical Imaging

“…The brain of human contains of millions of neurons, which are interconnected by synapses, NN is a fixed of connected input/output nodes, which has a weight associated for each connection. In the learning phase, the network adjusts the weights, so it is able for predicting the correct class label of the input [1]. Also it represents the neural connections mathematically of multiple hidden layers as shown in Figure 7 [3].…”

“…Machine learning is a subfield of artificial intelligence, including techniques for changing and updating knowledge, accumulating in intelligent systems to be smart, and the system ability to learn in a changing environment. That system can learn and adjust to such updates that carried out by machine learning techniques, it is a scientific discipline involved with the design and development of techniques that optimize a performance using past experience or example data [1]. Applications of machine learning contain medical diagnosis, natural language processing, financial data analysis, video surveillance, and bioinformatics [2].…”