Breast Mammogram Analysis and Classification Using Deep Convolution Neural Network

One of the fast-growing disease affecting women's health seriously isbreast cancer. It is highly essential to identify and detect breast cancer in the ear-lier stage. This paper used a novel advanced methodology than machine learningalgorithms such as Deep learning algorithms to classify breast cancer accurately.Deep learning algorithms are fully automatic in learning, extracting, and classify-ing the features and are highly suitable for any image, from natural to medicalimages. Existing methods focused on using various conventional and machinelearning methods for processing natural and medical images. It is inadequatefor the image where the coarse structure matters most. Most of the input imagesare downscaled, where it is impossible to fetch all the hidden details to reachaccuracy in classification. Whereas deep learning algorithms are high efficiency,fully automatic, have more learning capability using more hidden layers, fetch asmuch as possible hidden information from the input images, and provide an accu-rate prediction. Hence this paper uses AlexNet from a deep convolution neuralnetwork for classifying breast cancer in mammogram images. The performanceof the proposed convolution network structure is evaluated by comparing it withthe existing algorithms