ArticleSensors (Basel, Switzerland)2021
Computer Vision-Based Microcalcification Detection in Digital Mammograms Using Fully Connected Depthwise Separable Convolutional Neural Network.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 62 citations in OpenAlex.
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- Identification of medicinal plant parts using depth-wise separable convolutional neural network.PloS one · 2025Article
- Quantum classical hybrid convolutional neural networks for breast cancer diagnosis.Scientific reports · 2024Article
- Hybrid Feature Mammogram Analysis: Detecting and Localizing Microcalcifications Combining Gabor, Prewitt, GLCM Features, and Top Hat Filtering Enhanced with CNN Architecture.Diagnostics (Basel, Switzerland) · 2024Article
- Reproducibility and Explainability of Deep Learning in Mammography: A Systematic Review of Literature.The Indian journal of radiology & imaging · 2024Review
- Breast cancer diagnosis using support vector machine optimized by improved quantum inspired grey wolf optimization.Scientific reports · 2024Article
- Enhancing early breast cancer diagnosis through automated microcalcification detection using an optimized ensemble deep learning framework.PeerJ. Computer science · 2024Article
- A comprehensive analysis of recent advancements in cancer detection using machine learning and deep learning models for improved diagnostics.Journal of cancer research and clinical oncology · 2023Review
- Breast Cancer Diagnosis Based on IoT and Deep Transfer Learning Enabled by Fog Computing.Diagnostics (Basel, Switzerland) · 2023Article
- BRMI-Net: Deep Learning Features and Flower Pollination-Controlled Regula Falsi-Based Feature Selection Framework for Breast Cancer Recognition in Mammography Images.Diagnostics (Basel, Switzerland) · 2023Article
- Fully connected neural network-based serum surface-enhanced Raman spectroscopy accurately identifies non-alcoholic steatohepatitis.Hepatology international · 2023Article
- Using Deep Neural Network Approach for Multiple-Class Assessment of Digital Mammography.Healthcare (Basel, Switzerland) · 2022Article
- Breast lesions classifications of mammographic images using a deep convolutional neural network-based approach.PloS one · 2022Article
- Microcalcification Discrimination in Mammography Using Deep Convolutional Neural Network: Towards Rapid and Early Breast Cancer Diagnosis.Frontiers in public health · 2022Article
- State-of-the-Art Challenges and Perspectives in Multi-Organ Cancer Diagnosis via Deep Learning-Based Methods.Cancers · 2021Review
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Corrections and comments
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Authors and funding
6 authors at 3 institutions in 3 countries.
Funding
Abstract
Microcalcification clusters in mammograms are one of the major signs of breast cancer. However, the detection of microcalcifications from mammograms is a challenging task for radiologists due to their tiny size and scattered location inside a denser breast composition. Automatic CAD systems need to predict breast cancer at the early stages to support clinical work. The intercluster gap, noise between individual MCs, and individual object's location can affect the classification performance, which may reduce the true-positive rate. In this study, we propose a computer-vision-based FC-DSCNN CAD system for the detection of microcalcification clusters from mammograms and classification into malignant and benign classes. The computer vision method automatically controls the noise and background color contrast and directly detects the MC object from mammograms, which increases the classification performance of the neural network. The breast cancer classification framework has four steps: image preprocessing and augmentation, RGB to grayscale channel transformation, microcalcification region segmentation, and MC ROI classification using FC-DSCNN to predict malignant and benign cases. The proposed method was evaluated on 3568 DDSM and 2885 PINUM mammogram images with automatic feature extraction, obtaining a score of 0.97 with a 2.35 and 0.99 true-positive ratio with 2.45 false positives per image, respectively. Experimental results demonstrated that the performance of the proposed method remains higher than the traditional and previous approaches.
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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.