# Applying Deep Learning Methods for Mammography Analysis and Breast Cancer Detection
## 1 Introduction  
Being the most common type of cancer in the world, breast cancer (BC) caused over 2.3 million cases and more than 685,000 deaths in 2020 [1]. It continues to have a profound impact on the global number of deaths caused by a type of cancer as it is one of the most prevalent forms of this condition [2], especially for women.  
BC corresponds to cell alteration in the breast tissue, dividing irregularly, resulting in tumors; it is mostly perceived as a painless protuberance or a thickened region in the breast. There are three main categories of BC: \"Invasive Carcinoma\", which is the most predominant type, \"Ductal Carcinoma\" and \"Invasive Lobular Carcinoma\". An early detection of any abnormal sign or any potential symptom can drastically influence the treatment of BC.  
BC is considered a challenging condition as it is correlated to multiple factors, such as gender, genetics, climate or daily activities. Early screening of this disease is the most effective path towards fighting it; embedding AI technologies into the screening methods may drastically influence the early diagnosis and increase the treatment success rate.  
In the recent years, there has been a continuous interest in this domain that may materialize into an encouraging future. ML, a subtype of AI, comprises a series of computational algorithms that integrate extracted image features in order to assist the disease outcomes. DL is the most recent domain of ML and it is based on artificial neural networks for classifying and recognizing images.  
Medical imaging methods play a critical role in the early diagnosis of BC and in reducing the number of deaths caused by this disease. Different screening programs have been established in order to detect breast cancer in the early stages, facilitating an enhanced treatment along with increased survival rates. Additionally, BC imaging methods are crucial for monitoring or assessing the treatment and represent an essential part in clinicalprotocols as they can provide a broad range of relevant information based on the changes in morphology, structure, metabolism or functionality levels.  
The contribution of this paper to the literature is twofold: (a) To provide a critical review of medical imagining techniques for BC detection, as well as the deep learning methods for BC detection and classification; (b) To present the experimental study proposed for the detection of BC based on several deep learning techniques on mammograms.  
The innovative approach is represented by the generation of synthetic images which involves augmenting the dataset with realistic digital breast images using ML algorithms that can mimic the appearance of actual mammograms. By training the algorithm on a large dataset of real mammograms, it can learn to create new images that have similar characteristics to real images, including breast density and tissue structures. This approach proved its potential for improving the accuracy of breast cancer classification.  
The content of the paper is organized as follows: A critical review of the current methods for the early detection of BC is presented in Section 2. The materials and methods used for the study are presented in Section 3, followed by the results in Section 4. Section 5 encompasses the discussion of the main findings of our paper and some conclusions are drawn in Section 6. The references are also included at the end of the manuscript.