Journal ArticleDOI
A novel bi-modal extended Huber loss function based refined mask RCNN approach for automatic multi instance detection and localization of breast cancer
TLDR
The experimental result shows that the proposed bi-modal extended Huber loss function based refined mask regional convolutional neural network is a better suited approach for multi-instance detection, localization and classification of breast cancer.Abstract:
Breast cancer is an extremely aggressive cancer in women. Its abnormalities can be observed in the form of masses, calcification and lumps. In order to reduce the mortality rate of women its detection is needed at an early stage. The present paper proposes a novel bi-modal extended Huber loss function based refined mask regional convolutional neural network for automatic multi-instance detection and localization of breast cancer. To refine and increase the efficacy of the proposed method three changes are casted. First, a pre-processing step is performed for mammogram and ultrasound breast images. Second, the features of the region proposal network are separately mapped for accurate region of interest. Third, to reduce overfitting and fast convergence, an extended Huber loss function is used at the place of SmoothL1(x) in boundary loss. To extend the functionality of Huber loss, the delta parameter is automated by the aid of median absolute deviation with grid search algorithm. It provides the best optimum value of delta instead of user-based value. The proposed method is compared with pre-existing methods in terms of accuracy, true positive rate, true negative rate, precision, F-score, balanced classification rate, Youden’s index, Jaccard Index and dice coefficient on CBIS-DDSM and ultrasound database. The experimental result shows that the proposed method is a better suited approach for multi-instance detection, localization and classification of breast cancer. It can be used as a diagnostic medium that helps in clinical purposes and leads to a precise diagnosis of breast cancer abnormalities.read more
Citations
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A Deep Learning-Based Reverse Logistics Model for Recycling Construction and Demolition Waste
TL;DR: In this paper , an end-to-end improved convolutional neural network (EEI-CNN) based reverse logistics model was proposed for recycling C&D waste to overcome the existing recycling methods, such as cost, human intervention, unstable identification process for recycling, onsite sorting techniques, irregular landfill events, and a lack of an effective waste tracking system.
Proceedings ArticleDOI
Design of a System and Method for Optimal selection of Tumor Slice using Linear Ultrasound Imaging for Histopathology
Abhishek Kumar,Debdoot Sheet +1 more
TL;DR: In this paper , a linear gantry scanner is used to acquire images and integrated with a deep learning model to predict the optimal slice representative of pathology in a tumor mass, achieving an F1 score of 0.97 and an accuracy of 97.5% in predicting an optimal slice using this approach.
References
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Journal ArticleDOI
Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries.
Hyuna Sung,Jacques Ferlay,Rebecca L. Siegel,Mathieu Laversanne,Isabelle Soerjomataram,Ahmedin Jemal,Freddie Bray +6 more
TL;DR: The GLOBOCAN 2020 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer (IARC) as mentioned in this paper show that female breast cancer has surpassed lung cancer as the most commonly diagnosed cancer, with an estimated 2.3 million new cases (11.7%), followed by lung cancer, colorectal (11 4.4%), liver (8.3%), stomach (7.7%) and female breast (6.9%), and cervical cancer (5.6%) cancers.
Journal ArticleDOI
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
TL;DR: This work introduces a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals and further merge RPN and Fast R-CNN into a single network by sharing their convolutionAL features.
Proceedings Article
Mask R-CNN
TL;DR: This work presents a conceptually simple, flexible, and general framework for object instance segmentation that outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners.
Journal ArticleDOI
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
Hoo-Chang Shin,Holger R. Roth,Mingchen Gao,Le Lu,Ziyue Xu,Isabella Nogues,Jianhua Yao,Daniel J. Mollura,Ronald M. Summers +8 more
TL;DR: Two specific computer-aided detection problems, namely thoraco-abdominal lymph node (LN) detection and interstitial lung disease (ILD) classification are studied, achieving the state-of-the-art performance on the mediastinal LN detection, and the first five-fold cross-validation classification results are reported.
Journal ArticleDOI
Mask R-CNN
TL;DR: Mask R-CNN as discussed by the authors extends Faster-RCNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition, which achieves state-of-the-art performance in instance segmentation.