title: ÐÐ»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð½Ð°Ð±Ð¾ÑÐ° Ð´Ð°Ð½Ð½ÑÑ ÑÐ½Ð¸Ð¼ÐºÐ¾Ð² Ð¼Ð°Ð¼Ð¼Ð¾Ð³ÑÐ°ÑÐ¸Ð¸ Ð² ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð¾Ð¹ ÑÐ¸ÑÑÐµÐ¼Ðµ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð½Ð°Ñ Ð¸ Ð´Ð¸Ð°Ð³Ð½Ð¾ÑÑÐ¸ÐºÐ° Ð·Ð°Ð±Ð¾Ð»ÐµÐ²Ð°Ð½Ð¸Ñ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Deep learning for analysis of mammography dataset for use in computer-aided detection and diagnosis research
Creators: name: Ð Ð¾Ð´ÑÐ¸Ð³ÐµÑ ÐÐ°ÑÑÐ¾Ð½, ÐÐ°ÑÐµÑÐ¸Ð½Ðµ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2020
Subjects: ÑÐ²ÐµÑÑÐ¾ÑÐ½Ð°Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ
Ð³Ð»ÑÐ±Ð¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð¾Ðµ Ð·ÑÐµÐ½Ð¸Ðµ
ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð°Ñ ÑÐ¸ÑÑÐµÐ¼Ð° Ð´Ð¸Ð°Ð³Ð½Ð¾ÑÑÐ¸ÐºÐ¸
Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¼ÐµÐ´Ð¸ÑÐ¸Ð½ÑÐºÐ¾Ð³Ð¾ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ñ
ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ ÑÐ°ÐºÐ° Ð¼Ð¾Ð»Ð¾ÑÐ½Ð¾Ð¹ Ð¶ÐµÐ»ÐµÐ·Ñ
Ð¼ÐµÑÐ¾Ð´ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð¾Ð¿ÑÑ Ð¾Ð»ÐµÐ¹
convolutional neural network
deep learning
computer vision
computer-aided diagnosis system
medical image processing
breast cancer classification.tumors detection method

description: Ð¢Ð¾ÑÐ½Ð¾Ðµ Ð´Ð¸Ð°Ð³Ð½Ð¾ÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ Ð·Ð°Ð±Ð¾Ð»ÐµÐ²Ð°Ð½Ð¸Ñ ÑÑÐµÐ±ÑÐµÑ Ð¼Ð½Ð¾Ð³Ð¾Ð»ÐµÑÐ½ÐµÐ¹ Ð¼ÐµÐ´Ð¸ÑÐ¸Ð½ÑÐºÐ¾Ð¹ Ð¿ÑÐ°ÐºÑÐ¸ÐºÐ¸. Ð Ð¿Ð¾ÑÐ»ÐµÐ´Ð½ÐµÐµ Ð²ÑÐµÐ¼Ñ ÐÐ°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ, Ð¾ÑÐ¾Ð±ÐµÐ½Ð½Ð¾ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ñ ÐÐ»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ, Ð´Ð¾Ð±Ð¸Ð»Ð¾ÑÑ Ð¾Ð³ÑÐ¾Ð¼Ð½ÑÑ ÑÑÐ¿ÐµÑÐ¾Ð² Ð² Ð¾Ð±Ð»Ð°ÑÑÐ¸ Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ¾Ð¹ Ð´Ð¸Ð°Ð³Ð½Ð¾ÑÑÐ¸ÐºÐ¸ Ð·Ð°Ð±Ð¾Ð»ÐµÐ²Ð°Ð½Ð¸Ð¹, Ð±Ð»Ð°Ð³Ð¾Ð´Ð°ÑÑ ÑÐµÐ¼Ñ Ñ Ð²ÑÐ°ÑÐµÐ¹ Ð¿Ð¾ÑÐ²Ð¸Ð»ÑÑ Ð¸Ð½ÑÑÑÑÐ¼ÐµÐ½Ñ, Ð¿Ð¾Ð¼Ð¾Ð³Ð°ÑÑÐ¸Ð¹ Ð¸Ð¼ Ð¿ÑÐ¸Ð½Ð¸Ð¼Ð°ÑÑ ÑÐµÑÐµÐ½Ð¸Ñ Ð²Ð¾ Ð²ÑÐµÐ¼Ñ Ð´Ð¸Ð°Ð³Ð½Ð¾ÑÑÐ¸ÑÐµÑÐºÐ¾Ð³Ð¾ Ð¿ÑÐ¾ÑÐµÑÑÐ°. 
 ÐÐ (ÐÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÐ¹ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑ) Ð¸Ð¼ÐµÐµÑ Ð±Ð¾Ð»ÑÑÐ¾Ð¹ Ð¿Ð¾ÑÐµÐ½ÑÐ¸Ð°Ð» Ð´Ð»Ñ ÐµÐ³Ð¾ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ Ð¿ÑÐ°ÐºÑÐ¸ÑÐµÑÐºÐ¸ Ð²Ð¾ Ð²ÑÐµÑ Ð¾Ð±Ð»Ð°ÑÑÑÑ Ð¼ÐµÐ´Ð¸ÑÐ¸Ð½Ñ. ÐÐµÐ´Ð¸ÑÐ¸Ð½ÑÐºÐ¸Ðµ Ð¿ÑÐ¾ÐµÐºÑ ÑÐ¾ÑÑÐ¾Ð¸Ñ Ð² ÑÐ¾Ð¼, ÑÑÐ¾Ð±Ñ ÑÑÐ¸ÑÑÐ²Ð°ÑÑ Ð´Ð°Ð½Ð½ÑÐµ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð½Ð¾Ð³Ð¾ ÑÐ¸Ð¿Ð°, Ð½Ð°Ð¿ÑÐ¸Ð¼ÐµÑ, ÑÐ¸ÑÐ»Ð¾Ð²ÑÐµ Ð´Ð°Ð½Ð½ÑÐµ, ÑÐ°ÐºÐ¸Ðµ ÐºÐ°Ðº ÑÐ°ÑÑÐ¾ÑÐ° ÑÐµÑÐ´ÐµÑÐ½ÑÑ ÑÐ¾ÐºÑÐ°ÑÐµÐ½Ð¸Ð¹ Ð¸Ð»Ð¸ Ð°ÑÑÐµÑÐ¸Ð°Ð»ÑÐ½Ð¾Ðµ Ð´Ð°Ð²Ð»ÐµÐ½Ð¸Ðµ, Ð¸Ð»Ð¸ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð¸Ð¸ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹, ÑÐ°ÐºÐ¸Ñ ÐºÐ°Ðº ÑÐµÐ½ÑÐ³ÐµÐ½Ð¾Ð²ÑÐºÐ¸Ðµ ÑÐ½Ð¸Ð¼ÐºÐ¸ Ð¸Ð»Ð¸ Ð¾Ð±ÑÐ°Ð·ÑÑ Ð±Ð¸Ð¾Ð¿ÑÐ¸Ð¹Ð½Ð¾Ð¹ ÑÐºÐ°Ð½Ð¸ Ð² ÐºÐ°ÑÐµÑÑÐ²Ðµ Ð²ÑÐ¾Ð´Ð½ÑÑ Ð´Ð°Ð½Ð½ÑÑ. ÐÐ°ÑÐµÐ¼ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¸Ð·Ð²Ð»ÐµÑÐµÐ½Ð½ÑÑ Ð´Ð°Ð½Ð½ÑÑ ÑÐ¾Ð·Ð´Ð°ÑÑÑÑ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ñ, ÑÐ¿Ð¾ÑÐ¾Ð±Ð½ÑÑ Ð²ÑÑÐ°Ð±Ð¾ÑÐ°ÑÑ Ð»Ð¸Ð±Ð¾ Ð½ÐµÐºÑÑ Ð²ÐµÑÐ¾ÑÑÐ½Ð¾ÑÑÑ, Ð»Ð¸Ð±Ð¾ ÑÐ¾Ð·Ð´Ð°ÑÑ Ð½ÐµÐºÑÑ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ. Ð­ÑÐ¾Ñ Ð¿ÑÐ¾ÐµÐºÑ Ð±ÑÐ» ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½ Ñ ÑÐµÐ»ÑÑ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ ÑÐ¸Ð¿Ð¾Ð² ÑÐ°ÐºÐ° Ð¼Ð¾Ð»Ð¾ÑÐ½Ð¾Ð¹ Ð¶ÐµÐ»ÐµÐ·Ñ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° ÑÐµÐ½ÑÐ³ÐµÐ½Ð¾Ð²ÑÐºÐ¸Ñ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹.
 Ð ÑÑÐ¾Ð¹ ÑÑÐ°ÑÑÐµ Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½ Ð¼ÐµÑÐ¾Ð´ Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ¾Ð¹ ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ð¸ Ð¾Ð¿ÑÑÐ¾Ð»Ð¸ Ð¼Ð¾Ð»Ð¾ÑÐ½Ð¾Ð¹ Ð¶ÐµÐ»ÐµÐ·Ñ, Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð½ÑÐ¹ Ð½Ð° Ð¼Ð½Ð¾Ð³Ð¾Ð»ÑÑÐµÐ²Ð¾Ð¹ Ð¡Ð²ÐµÑÑÐ¾ÑÐ½Ð¾Ð¹ ÐÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ Ð¡ÐµÑÐ¸ (Convolutional Neural Network, CNN). ÐÐ±ÑÑÐµÐ½Ð¸Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¸ Ð½Ð°ÑÑÑÐ¾Ð¹ÐºÐ° Ð³Ð¸Ð¿ÐµÑÐ¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð² Ð±ÑÐ»Ð¸ Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ñ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð½Ð°Ð±Ð¾ÑÐ° Ð´Ð°Ð½Ð½ÑÑ DDSM (Â«Digital Database for Screening MammographyÂ» / Â«Ð¦Ð¸ÑÑÐ¾Ð²Ð°Ñ Ð±Ð°Ð·Ð° Ð´Ð°Ð½Ð½ÑÑ Ð´Ð»Ñ ÑÐºÑÐ¸Ð½Ð¸Ð½Ð³Ð¾Ð²Ð¾Ð¹ Ð¼Ð°Ð¼Ð¼Ð¾Ð³ÑÐ°ÑÐ¸Ð¸Â»).
 Ð Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÑÐ°ÑÑÐµ Ð²Ñ ÑÐ°Ðº Ð¶Ðµ Ð½Ð°Ð¹Ð´ÐµÑÐµ Ð¿Ð¾Ð´ÑÐ¾Ð±Ð½Ð¾Ðµ Ð¾Ð¿Ð¸ÑÐ°Ð½Ð¸Ðµ ÐºÐ°Ð¶Ð´Ð¾Ð³Ð¾ Ð¸Ð· ÑÑÐ°Ð¿Ð¾Ð² Ð¿ÑÐ¾ÐµÐºÑÐ°, Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ðµ Ð¿ÑÐ¾Ð±Ð»ÐµÐ¼Ñ, Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ðµ Ð¿ÑÐ¾Ð±Ð»ÐµÐ¼Ñ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¸ Ð¿ÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ð¸Ðµ ÐµÐ³Ð¾ ÑÐµÑÐµÐ½Ð¸Ñ, Ð¿Ð¾Ð¸ÑÐº Ð¸ Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð¸Ðµ Ð½Ð°ÑÐµÐ³Ð¾ Ð½Ð°Ð±Ð¾ÑÐ° Ð´Ð°Ð½Ð½ÑÑ, Ð¿ÑÐµÐ¾Ð±ÑÐ°Ð·Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð´Ð°Ð½Ð½ÑÑ, Ð¼Ð¾Ð´ÐµÐ»Ñ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¸ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð´Ð»Ñ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ.
description: Correctly diagnosing diseases takes years of medical training. Machine Learning particularly Deep Learning algorithms, has recently made huge advances in automatically diagnosing diseases, making doctors have a tool to help them make decisions and support the diagnostic process.
 AI has the potential to be applied in almost every field of medicine. The projects in medicine read in some type of data, either numerical such as heart rate or blood pressure or image-based such as ray-x scans or Images of Biopsy Tissue Samples as an input. The algorithms then learn from the data and churn out either a probability or a classification. Oriented in this same direction, this project was developed with the objective of training an algorithm to classify types of breast cancer based on the analysis of x-ray images. 
 In this paper, an automatic breast tumor segmentation approach based on a multi-path Convolutional Neural Network (CNN) is presented. The modelâs training and hyper-parameter tuning were performed on the DDSM (" Digital Database for Screening Mammography") dataset. 
 In addition, there is a detailed description of each one of the phases of the project, definition problem, define a machine learning problem and propose a solution, search and construction our dataset, transform data, train model and use the model to make predictions.