Detection and identification of stages Of breast cancer on big data Environment of mamographic images

Abstract

Diagnosing breast cancer at early stage minimizes the mortality rate; newlinedepicting the importance of novel detection methods. Breast cancer detection at newlinelater phase is the reason that occupies second position worldwide as the major newlinecause of women death due to malignancy. Causative factors of breast cancer are newlinenot yet unraveled and hence effective solutions of prevention are not discovered newlinetill now. The survival rate can be improved with an initial diagnosis and foremost newlinecare, providing an essential health maintenance requirement. Screening, detection newlineand diagnosis are possible with different tools that utilizes the day-to-day data newlinecollected from patients in identifying the hidden markings through data newlineprocessing, thereby aids in improvisation of the available medical facilities. newlineAdditionally, the targeted high cost in curing the cancer can be well-minimized. newlineMachine learning, an advancement in computer science is one of the novel ways newlinethat uses the training data for learning and knowledge extraction to enable the newlineassigned tasks. Features/attributes of varied values and types are the normal ways newlineused to represent the data used. The type of data given forecasts the mode of newlinemachine learning technique to be utilized for attaining the desired result. newlineChallenges in classification learning includes handling duplicate and missing newlineattributes. Also, the accuracy will become low without preprocessing, feature newlineselection and optimization. Artifacts (an unwanted portion of a digital image) and newlinenoise removal from the original digital images, region detection and edge newlinepreserving of digital images are challenges in image processing newline