Classification Breast Cancer Revisited with Machine Learning

Diagnosing Breast Cancer by Machine Learning

Background and Aim: Cancer and in particular Breast cancer are among the diseases that have the highest mortality rate in Iran after heart disease. The accurate prognosis for Breast cancer is important, and the presence of various symptoms and features of this disease makes it difficult for doctors to diagnose. This study aimed to identify the factors affecting Breast cancer, modeling and ultim...

ADABOOST ENSEMBLE ALGORITHMS FOR BREAST CANCER CLASSIFICATION

With an advance in technologies, different tumor features have been collected for Breast Cancer (BC) diagnosis, processing of dealing with large data set suffers some challenges which include high storage capacity and time require for accessing and processing. The objective of this paper is to classify BC based on the extracted tumor features. To extract useful information and diagnose the tumo...

Machine Learning an Experimental Science (revisited) * Machine Learning as an Experimental Science (revisited) *

In 1988, Langley wrote an influential editorial in the journal Machine Learning titled “Machine Learning as an Experimental Science”, arguing persuasively for a greater focus on performance testing. Since that time the emphasis has become progressively stronger. Nowadays, to be accepted to one of our major conferences or journals, a paper must typically contain a large experimental section with...

Involvement of Machine Learning for Breast Cancer Image Classification: A Survey

Breast cancer is one of the largest causes of women's death in the world today. Advance engineering of natural image classification techniques and Artificial Intelligence methods has largely been used for the breast-image classification task. The involvement of digital image classification allows the doctor and the physicians a second opinion, and it saves the doctors' and physicians' time. Des...

An Investigation of the Breast Cancer Classification Using Various Machine Learning Techniques