A review on machine learning techniques for the assessment of image grading in breast mammogram

Breast cancer is the 2nd leading cancer of death among women around the world. In Asia and Africa due to low income, the mortality rates are very high as compared to Europe and America. Initially, image interpretation is manually conducted by the radiologist and physicians that requires expertise; thus, the computer-aided diagnostic is necessary to enhance the accuracy of cancer diagnostics in mammograms at early stages. To overcome human error computer-aided system was developed based on machine learning and deep learning algorithm to process medical images with efficient accuracy for the diagnosis of cancer and assist the physician for better decisions making. This research aims to present the state-of-the-art machine learning techniques for the detection of breast cancer, and critically analysis of the current literature in this area to identify the research gap. There are many studies presented in the literature to achieve similar goals. The main difference between these studies and this review is that this paper is more focused on those modalities that can figure out breast composition, mass, density, calcification, and architectural distortion. This study includes a summary of 110 papers, pointing out which techniques are applied for image preprocessing and classification, which method is implemented for the detection of breast density, mass, and calcification from mammogram images. Furthermore, we critically analyzed the performance measuring parameters for the evaluation of results and the datasets that have been used for experiments. Another focus in this review is to assess the modalities and features that can be helpful for the assessment of grading in mammogram images.