| Abstrakt: |
This paper examines and assesses state-of-the-art proposed machine and deep learning techniques for breast cancer identification and classification based on breast screening image modalities. Ten research questions related to the medical image modalities, image dataset, image pre-processing, segmentation and classification techniques are framed to identify the scope of the review. From the perspective of research questions, an extensive review is carried out with various research papers, book chapters published in SCI/Scopus-indexed journals and international conferences from 2010 to 2021. Many issues such as image modalities, segmentation techniques, features, and evaluation metrics are identified with the machine and deep learning methodologies. This review shows that about 57% of the selected studies have used digital mammograms for breast cancer identification and classification. Most of the selected studies have used public datasets and employed noise removal, data augmentation, scaling, and image normalization techniques to alleviate the inconsistencies in breast cancer images. It is observed that mainly thresholding-based, region-based, edge-based, clustering-based, and deep learning (DL) based segmentation techniques are used in many studies. It has also been observed that the support vector machine (SVM) and variants of convolutional neural network (CNN) are the most used classifiers for breast cancer identification and classification. It is found that CNN-based classification models have achieved an accuracy of 100% in the classification of breast cancer for 250 ultrasound images. This review may help researchers to figure out whether a machine or deep learning technique works better on a particular dataset and which features are significant for breast cancer detection. Traditional machine-learning approaches are mostly used in classification, whereas deep-learning techniques have conquered the field of image analysis. This review presents the strengths and weaknesses of the existing machine and deep learning-based models. This review is summarized by providing appropriate answers to the formed research questions with future recommendations in the identification and classification of breast cancer. Highlights: Illustrate the applications of the machine and deep learning techniques in breast cancer detection. Illustrate the medical image modalities used to identify and classify breast cancer. Present the breast image datasets used in the classification models for medical images. Illustrate the image pre-processing, image segmentation, feature extraction techniques and classification algorithms. Provide machine learning techniques for breast cancer identification and classification using various medical image modalities. Provide deep learning techniques for breast cancer identification and classification using various medical image modalities. Identify research gaps and recommendations for the future are proposed. [ABSTRACT FROM AUTHOR] |
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