Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review

Jiménez-Gaona, Yuliana; Rodríguez-Álvarez, M. J.; Lakshminarayanan, Vasudevan

doi:10.3390/app10228298

Cited by 61 publications

(31 citation statements)

References 194 publications

(322 reference statements)

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“…Researchers present a number of computer vision-based automated methods for breast cancer classification using ultrasound images [ 44 , 45 ]. A few of them concentrated on the segmentation step, followed by feature extraction [ 46 ], and a few extracted features from raw images.…”

Section: Related Workmentioning

confidence: 99%

Breast Cancer Classification from Ultrasound Images Using Probability-Based Optimal Deep Learning Feature Fusion

Jabeen

Khan

Alhaisoni

et al. 2022

Sensors

155

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After lung cancer, breast cancer is the second leading cause of death in women. If breast cancer is detected early, mortality rates in women can be reduced. Because manual breast cancer diagnosis takes a long time, an automated system is required for early cancer detection. This paper proposes a new framework for breast cancer classification from ultrasound images that employs deep learning and the fusion of the best selected features. The proposed framework is divided into five major steps: (i) data augmentation is performed to increase the size of the original dataset for better learning of Convolutional Neural Network (CNN) models; (ii) a pre-trained DarkNet-53 model is considered and the output layer is modified based on the augmented dataset classes; (iii) the modified model is trained using transfer learning and features are extracted from the global average pooling layer; (iv) the best features are selected using two improved optimization algorithms known as reformed differential evaluation (RDE) and reformed gray wolf (RGW); and (v) the best selected features are fused using a new probability-based serial approach and classified using machine learning algorithms. The experiment was conducted on an augmented Breast Ultrasound Images (BUSI) dataset, and the best accuracy was 99.1%. When compared with recent techniques, the proposed framework outperforms them.

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Section: Related Workmentioning

confidence: 99%

Breast Cancer Classification from Ultrasound Images Using Probability-Based Optimal Deep Learning Feature Fusion

Jabeen

Khan

Alhaisoni

et al. 2022

Sensors

155

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“…One downside of mammography is that its results are dependent on the lesion type, patient's age, and breast density. To be more specific, when it comes to denser breasts, they are "radiographically" harder to see and exhibit a lower contrast between cancerous parts and their backgrounds [11].…”

Section: A Breast Imaging Modalitiesmentioning

confidence: 99%

Design Guidelines for Mammogram-Based Computer-Aided Systems Using Deep Learning Techniques

Azour

Boukerche

2022

IEEE Access

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Breast cancer is the second fatal disease among cancers patients both in Canada and across the globe. However, when detected early, a patients' survival rate can be raised. Thus, researchers and scientists have been practicing to develop Computer-Aided Detection (CADe) and Computer-Aided Diagnosis (CADx) systems. Traditional CAD systems depend on manual feature extraction, which has provided radiologists with poor detection and diagnosis tools. Nevertheless, recently, the powerful application of Convolutional Neural Networks (CNN)s as one of the deep learning-based methods has revolutionized these systems' accuracy and development. This article proposes categorizing the current deep learning research on mammogram types based on researchers' techniques for their empirical studies. Also, we provide an overview of different publicly available data resources and available datasets for breast imaging. This critical review of the state-of-the-art techniques is presented, which we believe can serve as a valuable source for research scientists investigating deep learning-based breast mammogram classification.

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“…Computer-aided diagnosis has its origins in the 1980s at the Kurt Rossmann Laboratories for Radiologic Image Research in the Department of Radiology at the University of Chicago [153]. The initial work was on the detection of breast cancer [35,153,154], and the reader is referred to a recent review [155].…”

Section: Computer-aided Diagnosis In Medical Imaging (Cadx System)mentioning

confidence: 99%

MR Images, Brain Lesions, and Deep Learning

2021

Self Cite

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Medical brain image analysis is a necessary step in computer-assisted/computer-aided diagnosis (CAD) systems. Advancements in both hardware and software in the past few years have led to improved segmentation and classification of various diseases. In the present work, we review the published literature on systems and algorithms that allow for classification, identification, and detection of white matter hyperintensities (WMHs) of brain magnetic resonance (MR) images, specifically in cases of ischemic stroke and demyelinating diseases. For the selection criteria, we used bibliometric networks. Of a total of 140 documents, we selected 38 articles that deal with the main objectives of this study. Based on the analysis and discussion of the revised documents, there is constant growth in the research and development of new deep learning models to achieve the highest accuracy and reliability of the segmentation of ischemic and demyelinating lesions. Models with good performance metrics (e.g., Dice similarity coefficient, DSC: 0.99) were found; however, there is little practical application due to the use of small datasets and a lack of reproducibility. Therefore, the main conclusion is that there should be multidisciplinary research groups to overcome the gap between CAD developments and their deployment in the clinical environment.

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Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review

Cited by 61 publications

References 194 publications

Breast Cancer Classification from Ultrasound Images Using Probability-Based Optimal Deep Learning Feature Fusion

Breast Cancer Classification from Ultrasound Images Using Probability-Based Optimal Deep Learning Feature Fusion

Design Guidelines for Mammogram-Based Computer-Aided Systems Using Deep Learning Techniques

MR Images, Brain Lesions, and Deep Learning

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