Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review

By Essam H. Houssein, M. Emam, Abdelmgeid A. Ali, Ponnuthurai Nagaratnam Suganthan. Expert systems with applications, 2021

347 citations recorded by the source corpus.

Abstract Breast cancer is the second leading cause of death for women, so accurate early detection can help decrease breast cancer mortality rates. Computer-aided detection allows radiologists to detect abnormalities efficiently. Medical images are sources of information relevant to the detection and diagnosis of various diseases and abnormalities. Several modalities allow radiologists to study the internal structure, and these modalities have been met with great interest in several types of research. In some medical fields, each of these modalities is of considerable significance. This study aims at presenting a review that shows the new applications of machine learning and deep learning technology for detecting and classifying breast cancer and provides an overview of progress in this area. This review reflects on the classification of breast cancer utilizing multi-modalities medical imaging. Details are also given on techniques developed to facilitate the classification of tumors, non-tumors, and dense masses in various medical imaging modalities. It first provides an overview of the different approaches to machine learning, then an overview of the different deep learning techniques and specific architectures for the detection and classification of breast cancer. We also provide a brief overview of the different image modalities to give a complete overview of the area. In the same context, this review was performed using a broad variety of research databases as a source of information for access to various field publications. Finally, this review summarizes the future trends and challenges in the classification and detection of breast cancer.

Subfields: Artificial Intelligence, Neurology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/335a5ae305f35420f30262b31ec7668ab0d7e79c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. M. Ahsan
2. Meghavi Rana
3. Zahed Siddique
4. Megha Bhushan

In Neurology

1. M. Ahsan
2. Meghavi Rana
3. Zahed Siddique
4. Megha Bhushan

In Radiology, Nuclear Medicine and Imaging

1. M. Ahsan
2. Meghavi Rana
3. Zahed Siddique
4. Megha Bhushan

Citations in the corpus

• Machine-Learning-Based Disease Diagnosis: A Comprehensive Review (2021, methodology): “To overcome such problems, selecting appropriate kernel and parameters is two key factors when applying SVM in data analysis [11].”
• AAU-Net: An Adaptive Attention U-Net for Breast Lesions Segmentation in Ultrasound Images (2022, neutral): “sound images [5], [14], [15], [16], [17].”
• Machine learning and deep learning approach for medical image analysis: diagnosis to detection (2022, methodology): “Recent ML and DL technologies were reviewed for the classification and detection of medical imaging modalities [39].”