Comparison of Current Deep Convolutional Neural Networks for the Segmentation of Breast Masses in Mammograms

Anaya-Isaza, Andrés; Mera-Jiménez, Leonel; Cabrera-Chavarro, Johan Manuel; Guachi-Guachi, Lorena; Peluffo-Ordonez, Diego

doi:10.1109/access.2021.3127862

Cited by 17 publications

(8 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…28 This current review extracted and organized the data in tabular form and summarized the use of MG in the diagnosis of breast cancer (Table 1). 2,4,5,7,9,24, Table 1. Models, classes and performance for breast X-ray mammography data in selected papers.…”

Section: X-ray Mammographymentioning

confidence: 99%

“…1 Given these critical factors, there is an urgent need for extensive research in all areas of BC, from prevention to early detection and efficacious intervention. 2,3 Personalized BC interventions are highly dependent on accurate diagnosis. BC typically features discrete histological, molecular and clinical phenotypes; and it sometimes manifests with radiological heterogeneity.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, BC cells can readily escape to the circulatory or lymphatic system, where they generate new tumours and invade distant vital organs. 2 At present, BC is the largest contributor to cancer-related mortality among women between 20–50 years of age. Based on information from the American Cancer Society, in 2019 alone, there were an estimated 268 600 new incidences and 41 740 cancer-associated deaths in the United States.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Machine learning and new insights for breast cancer diagnosis

Guo,

Zhang,

Yuan

et al. 2024

J Int Med Res

View full text Add to dashboard Cite

Breast cancer (BC) is the most prominent form of cancer among females all over the world. The current methods of BC detection include X-ray mammography, ultrasound, computed tomography, magnetic resonance imaging, positron emission tomography and breast thermographic techniques. More recently, machine learning (ML) tools have been increasingly employed in diagnostic medicine for its high efficiency in detection and intervention. The subsequent imaging features and mathematical analyses can then be used to generate ML models, which stratify, differentiate and detect benign and malignant breast lesions. Given its marked advantages, radiomics is a frequently used tool in recent research and clinics. Artificial neural networks and deep learning (DL) are novel forms of ML that evaluate data using computer simulation of the human brain. DL directly processes unstructured information, such as images, sounds and language, and performs precise clinical image stratification, medical record analyses and tumour diagnosis. Herein, this review thoroughly summarizes prior investigations on the application of medical images for the detection and intervention of BC using radiomics, namely DL and ML. The aim was to provide guidance to scientists regarding the use of artificial intelligence and ML in research and the clinic.

show abstract

Section: X-ray Mammographymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine learning and new insights for breast cancer diagnosis

Guo,

Zhang,

Yuan

et al. 2024

J Int Med Res

View full text Add to dashboard Cite

show abstract

“…However, to increase the accuracy of the detection and classification models, CV researchers are needed to help in the development of models that enhance the existent systems. In the last year, researchers in the CV community have worked hard to propose deep learning (DL) models to tackle this vital area for the benefit of society's healthcare [13][14][15][16]. However, a significant part of the research is focused on generating suitable datasets to train the models, especially for the detection and classification of occluded faces.…”

Section: Introductionmentioning

confidence: 99%

A Computer Vision Model to Identify the Incorrect Use of Face Masks for COVID-19 Awareness

Crespo

Martínez

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

Face mask detection has become a great challenge in computer vision, demanding the coalition of technology with COVID-19 awareness. Researchers have proposed deep learning models to detect the use of face masks. However, the incorrect use of a face mask can be as harmful as not wearing any protection at all. In this paper, we propose a compound convolutional neural network (CNN) architecture based on two computer vision tasks: object localization to discover faces in images/videos, followed by an image classification CNN to categorize the faces and show if someone is using a face mask correctly, incorrectly, or not at all. The first CNN is built upon RetinaFace, a model to detect faces in images, whereas the second CNN uses a ResNet-18 architecture as a classification backbone. Our model enables an accurate identification of people who are not correctly following the COVID-19 healthcare recommendations on face mask use. To enable further global use of our technology, we have released both the dataset used to train the classification model and our proposed computer vision pipeline to the public, and optimized it for embedded systems deployment.

show abstract

“…It is utilized as a “second opinion” for radiologists to investigate anomalies from mammogram images through computational methods [ 4 ]. Various studies have suggested that a cloud-based CAD system benefits patients in remote and rural areas, especially in breast cancer [ 5 ]. The development of smart technologies created our everyday life more comfortable than earlier.…”

Section: Introductionmentioning

confidence: 99%

Identification of Anomalies in Mammograms through Internet of Medical Things (IoMT) Diagnosis System

Khan

Saba

Sadad

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Breast cancer is the primary health issue that women may face at some point in their lifetime. This may lead to death in severe cases. A mammography procedure is used for finding suspicious masses in the breast. Teleradiology is employed for online treatment and diagnostics processes due to the unavailability and shortage of trained radiologists in backward and remote areas. The availability of online radiologists is uncertain due to inadequate network coverage in rural areas. In such circumstances, the Computer-Aided Diagnosis (CAD) framework is useful for identifying breast abnormalities without expert radiologists. This research presents a decision-making system based on IoMT (Internet of Medical Things) to identify breast anomalies. The proposed technique encompasses the region growing algorithm to segment tumor that extracts suspicious part. Then, texture and shape-based features are employed to characterize breast lesions. The extracted features include first and second-order statistics, center-symmetric local binary pattern (CS-LBP), a histogram of oriented gradients (HOG), and shape-based techniques used to obtain various features from the mammograms. Finally, a fusion of machine learning algorithms including K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA are employed to classify breast cancer using composite feature vectors. The experimental results exhibit the proposed framework’s efficacy that separates the cancerous lesions from the benign ones using 10-fold cross-validations. The accuracy, sensitivity, and specificity attained are 96.3%, 94.1%, and 98.2%, respectively, through shape-based features from the MIAS database. Finally, this research contributes a model with the ability for earlier and improved accuracy of breast tumor detection.

show abstract

Comparison of Current Deep Convolutional Neural Networks for the Segmentation of Breast Masses in Mammograms

Cited by 17 publications

References 56 publications

Machine learning and new insights for breast cancer diagnosis

Machine learning and new insights for breast cancer diagnosis

A Computer Vision Model to Identify the Incorrect Use of Face Masks for COVID-19 Awareness

Identification of Anomalies in Mammograms through Internet of Medical Things (IoMT) Diagnosis System

Contact Info

Product

Resources

About