Journal ArticleDOI
Improved the detection and classification of breast cancer using hyper parameter tuning
K. Kousalya,T. Saranya +1 more
TLDR
For detecting and classifying breast cancer in breast cytology videos, deep learning frameworks are suggested and the suggested system will outperform CNN and DenseNet in the diagnosis and classification of breast tumors from histological photographs.About:
This article is published in Materials Today: Proceedings.The article was published on 2021-05-10. It has received 8 citations till now. The article focuses on the topics: Breast cancer & Cancer.read more
Citations
More filters
Journal ArticleDOI
An optimized deep learning architecture for breast cancer diagnosis based on improved marine predators algorithm
TL;DR: In this article , an improved marine predators algorithm (IMPA) was used to find the best values for the hyperparameters of the CNN architecture and the proposed method uses a pretrained CNN model called ResNet50 (residual network).
Journal ArticleDOI
Efficient Breast Cancer Diagnosis from Complex Mammographic Images Using Deep Convolutional Neural Network
TL;DR: In this paper , the authors proposed a computational framework for diagnosing breast cancer using a ResNet-50 convolutional neural network to classify mammogram images, which achieved an outstanding classification accuracy of 93%, surpassing other models trained on the same dataset.
Journal ArticleDOI
Breast Cancer Classification Based on Histopathological Images Using a Deep Learning Capsule Network
TL;DR: An enhanced capsule network that extracts multi-scale features using the Res2Net block and four additional convolutional layers is presented and the testing results show that the model outperformed the previous DL methods.
Journal ArticleDOI
A Survey of Convolutional Neural Network in Breast Cancer
TL;DR: In this paper , a comprehensive review of the diagnosis of breast cancer based on the convolutional neural network (CNN) after reviewing a sea of recent papers is presented, and the structure of CNN is given in the second part.
Posted ContentDOI
Coronary artery disease classification using support vector machines tuned via randomized search cross-validation
TL;DR: In this paper , the authors performed coronary artery disease detection with improved support vector machines using k-fold cross-validation experiments on the Z-Alizadeh Sani dataset to evaluate the performance of the models.
References
More filters
Journal ArticleDOI
Classification of breast cancer histology images using Convolutional Neural Networks
Teresa Araújo,Guilherme Aresta,Eduardo Castro,José Rouco,Paulo Aguiar,Catarina Eloy,António Polónia,Aurélio Campilho +7 more
TL;DR: A method for the classification of hematoxylin and eosin stained breast biopsy images using Convolutional Neural Networks (CNNs) is proposed and the sensitivity of the method for cancer cases is 95.6%.
Journal ArticleDOI
Benign and malignant breast tumors classification based on region growing and CNN segmentation
TL;DR: Two automated methods to diagnose mass types of benign and malignant in mammograms are presented and different classifiers (such as random forest, naive Bayes, SVM, and KNN) are used to evaluate the performance of the proposed methods.
Journal ArticleDOI
A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets.
TL;DR: A novel breast CADx methodology that can be used to more effectively characterize breast lesions in comparison to existing methods is proposed, which is computationally efficient and circumvents the need for image preprocessing.
Journal ArticleDOI
Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features
Haibo Wang,Angel Cruz-Roa,Ajay Basavanhally,Hannah Gilmore,Natalie Shih,Michael Feldman,John E. Tomaszewski,Fabio A. González,Anant Madabhushi +8 more
TL;DR: A cascaded approach for mitosis detection that intelligently combines a CNN model and handcrafted features (morphology, color, and texture features) is presented, which is accurate, fast, and requires fewer computing resources compared to existing methods, making this feasible for clinical use.
Proceedings ArticleDOI
Deep features for breast cancer histopathological image classification
TL;DR: The experimental evaluation of DeCaf features for BC recognition shows that these features can be a viable alternative to fast development of high-accuracy BC recognition systems, generally achieving better results than traditional hand-crafted textural descriptors and outperforming task-specific CNNs in some cases.