ArticleBioMed research international2017
Three-Class Mammogram Classification Based on Descriptive CNN Features.
Article in BioMed research international, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it, 170 citations in OpenAlex.
- Breast Cancer Detection and Diagnosis Using Mammographic Data: Systematic Review.Journal of medical Internet research · 2019Pooled it
- Detection of breast cancer in digital breast tomosynthesis with vision transformers.Scientific reports · 2024Article
- Reproducibility and Explainability of Deep Learning in Mammography: A Systematic Review of Literature.The Indian journal of radiology & imaging · 2024Review
- General retrieval network model for multi-class plant leaf diseases based on hashing.PeerJ. Computer science · 2024Article
- Mammography with deep learning for breast cancer detection.Frontiers in oncology · 2024Review
- State-of-the-Art of Breast Cancer Diagnosis in Medical Images via Convolutional Neural Networks (CNNs).Journal of healthcare informatics research · 2023Review
- Optimized S-Curve Transformation and Wavelets-Based Fusion for Contrast Elevation of Breast Tomograms and Mammograms.Diagnostics (Basel, Switzerland) · 2023Article
- A novel wavelet decomposition and transformation convolutional neural network with data augmentation for breast cancer detection using digital mammogram.Scientific reports · 2022Article
- A Novel Hybrid Machine Learning Based System to Classify Shoulder Implant Manufacturers.Healthcare (Basel, Switzerland) · 2022Article
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- DIAROP: Automated Deep Learning-Based Diagnostic Tool for Retinopathy of Prematurity.Diagnostics (Basel, Switzerland) · 2021Article
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- Computer-Aided Diagnosis Scheme for Distinguishing Between Benign and Malignant Masses on Breast DCE-MRI Images Using Deep Convolutional Neural Network with Bayesian Optimization.Journal of digital imaging · 2021Article
- GASTRO-CADx: a three stages framework for diagnosing gastrointestinal diseases.PeerJ. Computer science · 2021Article
- Early prediction of neoadjuvant chemotherapy response for advanced breast cancer using PET/MRI image deep learning.Scientific reports · 2020Article
- Artificial intelligence and convolution neural networks assessing mammographic images: a narrative literature review.Journal of medical radiation sciences · 2020Review
- Segmentation of cervical intervertebral disks in videofluorography by CNN, multi-channelization and feature selection.International journal of computer assisted radiology and surgery · 2020Article
- Methods Used in Computer-Aided Diagnosis for Breast Cancer Detection Using Mammograms: A Review.Journal of healthcare engineering · 2020Review
- Using Convolutional Neural Network with Cheat Sheet and Data Augmentation to Detect Breast Cancer in Mammograms.Computational and mathematical methods in medicine · 2020Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper, a novel classification technique for large data set of mammograms using a deep learning method is proposed. The proposed model targets a three-class classification study (normal, malignant, and benign cases). In our model we have presented two methods, namely, convolutional neural network-discrete wavelet (CNN-DW) and convolutional neural network-curvelet transform (CNN-CT). An augmented data set is generated by using mammogram patches. To enhance the contrast of mammogram images, the data set is filtered by contrast limited adaptive histogram equalization (CLAHE). In the CNN-DW method, enhanced mammogram images are decomposed as its four subbands by means of two-dimensional discrete wavelet transform (2D-DWT), while in the second method discrete curvelet transform (DCT) is used. In both methods, dense scale invariant feature (DSIFT) for all subbands is extracted. Input data matrix containing these subband features of all the mammogram patches is created that is processed as input to convolutional neural network (CNN). Softmax layer and support vector machine (SVM) layer are used to train CNN for classification. Proposed methods have been compared with existing methods in terms of accuracy rate, error rate, and various validation assessment measures. CNN-DW and CNN-CT have achieved accuracy rate of 81.83% and 83.74%, respectively. Simulation results clearly validate the significance and impact of our proposed model as compared to other well-known existing techniques.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.