A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms

Abstract

Breast cancer is one of the most common death causes amongst women all over the world. Early detection of breast cancer plays a critical role in increasing the survival rate. Various imaging modalities, such as mammography, breast MRI, ultrasound and thermography, are used to detect breast cancer. Though there is a considerable success with mammography in biomedical imaging, detecting suspicious areas remains a challenge because, due to the manual examination and variations in shape, size, other mass morphological features, mammography accuracy changes with the density of the breast. Furthermore, going through the analysis of many mammograms per day can be a tedious task for radiologists and practitioners. One of the main objectives of biomedical imaging is to provide radiologists and practitioners with tools to help them identify all suspicious regions in a given image. Computer-aided mass detection in mammograms can serve as a second opinion tool to help radiologists avoid running into oversight errors. The scientific community has made much progress in this topic, and several approaches have been proposed along the way. Following a bottom-up narrative, this paper surveys different scientific methodologies and techniques to detect suspicious regions in mammograms spanning from methods based on low-level image features to the most recent novelties in AI-based approaches. Both theoretical and practical grounds are provided across the paper sections to highlight the pros and cons of different methodologies. The paper’s main scope is to let readers embark on a journey through a fully comprehensive description of techniques, strategies and datasets on the topic.

Review of Different Methods of Abnormal Mass Detection in Digital Mammograms

“…Ozha, et al, 17 surveyed the different scientific methodologies and techniques to detect suspicious regions in mammograms, spanning from methods based on low-level image features to the most recent novelties in artificial intelligence (AI)-based approaches. This method proved a considerable success with mammography in biomedical imaging.…”

Image Augmentation Techniques for Mammogram Analysis

“…GANs (Generative Adversarial Networks) belong to the family of unsupervised deep learning algorithms capable of extracting hidden underlying properties from data and employing them in decision-making. The fundamental goal of a GAN is to develop new image samples (by a generator) that the discriminator will not be able to tell apart from the original ones (Both network branches compete against each other and gradually learn to produce better results) [ 49 ]. GANs are reliant on two main components, namely, generator and discriminator.…”

A Transfer Representation Learning Approach for Breast Cancer Diagnosis from Mammograms using EfficientNet Models

“…We found that transfer learning plays a substantial role in various deep learning algorithms based on our literature review. With a modest number of datasets, this method is useful in the medical arena [15,21]. Different existing models based on a short dataset with the CNN architecture and the transfer learning method have not been completely investigated till now.…”