Mammography is an exceptionally normal screening apparatus for diagnosing breast growth at beginning time as compare to other screening techniques to reduce female death rate. The techniques and algorithms that were extensively used are convolution neural networks, artificial neural networks, support vector machines, and so on. A comparison pertaining to the supremacy of deep learning techniques over existing machine learning techniques is also stated in terms of data requirements, learning, etc which is the need of the hour as the medical database is ever increasing phenomena demanding faster results. Though these studies are vast, this spectrum of research requires more rigorous investigation in terms of classification with minimal errors. In this paper, a proposed Deep Learning (DL) system is connected on large dataset to assess the prediction on the breast disease mammogram images as compare to state-of-art classification strategy. Despite the fact that this automation is known for its robustness still its execution relies on two key focuses that are: Clustering and Classification. The DL system result shows better qualitative result as compared to Multilayer Perceptron (MLP) method. The best precision of 86% for the given dataset is accomplished through proposed method when compared with different classifiers in terms of accuracy.

Breast Cancer, Ultrasound, Mammography, Computer Aided Diagnosis(CAD) , Convolution Neural Network (CNN), Multi-Layer Perceptron(MLP), Machine learning techniques, Accuracy

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