Combining Convolutional Neural Networks for Multi-context Microcalcification Detection in Mammograms

2019 ◽  
pp. 36-44
Author(s):  
Benedetta Savelli ◽  
Claudio Marrocco ◽  
Alessandro Bria ◽  
Mario Molinara ◽  
Francesco Tortorella
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Microcalcification Detection

scholarly journals Convolutional Neural Networks for the Segmentation of Microcalcification in Mammography Imaging

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Gabriele Valvano ◽  
Gianmarco Santini ◽  
Nicola Martini ◽  
Andrea Ripoli ◽  
Chiara Iacconi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
False Positive ◽  
False Positive Rate ◽  
Novel Approach ◽  
Positive Rate ◽  
Microcalcification Detection ◽  
Microcalcification Clusters

Cluster of microcalcifications can be an early sign of breast cancer. In this paper, we propose a novel approach based on convolutional neural networks for the detection and segmentation of microcalcification clusters. In this work, we used 283 mammograms to train and validate our model, obtaining an accuracy of 99.99% on microcalcification detection and a false positive rate of 0.005%. Our results show how deep learning could be an effective tool to effectively support radiologists during mammograms examination.

Automatic Microcalcification Detection in Multi-vendor Mammography Using Convolutional Neural Networks

2016 ◽  
pp. 35-42 ◽  
Author(s):  
Jan-Jurre Mordang ◽  
Tim Janssen ◽  
Alessandro Bria ◽  
Thijs Kooi ◽  
Albert Gubern-Mérida ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Microcalcification Detection

Traffic Lights Detection in adverse conditions using Convolutional Neural Networks

2018 ◽  
Author(s):  
George Symeonidis ◽  
Peter P. Groumpos ◽  
Evangelos Dermatas
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Traffic Lights ◽  
Adverse Conditions

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

Convolutional Neural Networks for Underwater Pipeline Segmentation using Imperfect Datasets

2021 ◽  
Author(s):  
Edgar Medina ◽  
Roberto Campos ◽  
Jose Gabriel R. C. Gomes ◽  
Mariane R. Petraglia ◽  
Antonio Petraglia
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Underwater Pipeline
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