Proceedings ArticleDOI
Detection and classification of the breast abnormalities in digital mammograms via regional Convolutional Neural Network
Mohammed A. Al-masni,Mugahed A. Al-antari,J. M. Park,G. Gi,Taeyeon Kim,Patricio Rivera,Edwin Valarezo,Seung-Moo Han,Tae-Seong Kim +8 more
- Vol. 2017, pp 1230-1233
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TLDR
A novel computer-aided diagnose (CAD) system based on one of the regional deep learning techniques: a ROI-based Convolutional Neural Network which is called You Only Look Once (YOLO) seems to be feasible as a CAD system capable of detection and classification at the same time.Abstract:
Automatic detection and classification of the masses in mammograms are still a big challenge and play a crucial role to assist radiologists for accurate diagnosis. In this paper, we propose a novel computer-aided diagnose (CAD) system based on one of the regional deep learning techniques: a ROI-based Convolutional Neural Network (CNN) which is called You Only Look Once (YOLO). Our proposed YOLO-based CAD system contains four main stages: mammograms preprocessing, feature extraction utilizing multi convolutional deep layers, mass detection with confidence model, and finally mass classification using fully connected neural network (FC-NN). A set of training mammograms with the information of ROI masses and their types are used to train YOLO. The trained YOLO-based CAD system detects the masses and classifies their types into benign or malignant. Our results show that the proposed YOLO-based CAD system detects the mass location with an overall accuracy of 96.33%. The system also distinguishes between benign and malignant lesions with an overall accuracy of 85.52%. Our proposed system seems to be feasible as a CAD system capable of detection and classification at the same time. It also overcomes some challenging breast cancer cases such as the mass existing in the pectoral muscles or dense regions.read more
Citations
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Journal ArticleDOI
Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks.
TL;DR: Using the full spatial resolutions of the input image could enable to learn better specific and prominent features, leading to an improvement in the segmentation performance.
Journal ArticleDOI
Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system
Mohammed A. Al-masni,Mugahed A. Al-antari,J. M. Park,G. Gi,Taeyeon Kim,Patricio Rivera,Edwin Valarezo,Mun-Taek Choi,Seung-Moo Han,Tae-Seong Kim +9 more
TL;DR: A novel Computer-Aided Diagnosis (CAD) system based on one of the regional deep learning techniques, a ROI-based Convolutional Neural Network (CNN) which is called You Only Look Once (YOLO) can handle detection and classification simultaneously in one framework.
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A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification.
TL;DR: The results demonstrate that the proposed integrated CAD system, through all stages of detection, segmentation, and classification, outperforms the latest conventional deep learning methodologies.
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Multiple skin lesions diagnostics via integrated deep convolutional networks for segmentation and classification.
TL;DR: An integrated diagnostic framework that combines a skin lesion boundary segmentation stage and a multiple skin lesions classification stage is proposed that could be used to support and aid dermatologists for further improvement in skin cancer diagnosis.
Journal ArticleDOI
Deep convolutional neural networks for mammography: advances, challenges and applications
TL;DR: This survey conducted a detailed review of the strengths, limitations, and performance of the most recent CNNs applications in analyzing MG images and lists the best practices that improve the performance of CNNs including the pre-processing of images and the use of multi-view images.
References
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Proceedings ArticleDOI
You Only Look Once: Unified, Real-Time Object Detection
TL;DR: Compared to state-of-the-art detection systems, YOLO makes more localization errors but is less likely to predict false positives on background, and outperforms other detection methods, including DPM and R-CNN, when generalizing from natural images to other domains like artwork.
Journal ArticleDOI
Cancer statistics, 2016
TL;DR: Overall cancer incidence trends are stable in women, but declining by 3.1% per year in men, much of which is because of recent rapid declines in prostate cancer diagnoses, and brain cancer has surpassed leukemia as the leading cause of cancer death among children and adolescents.
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