ArticleDiagnostics (Basel, Switzerland)2021
Dilated Semantic Segmentation for Breast Ultrasonic Lesion Detection Using Parallel Feature Fusion.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 102 citations in OpenAlex.
- Hybrid adaptive attention deep supervision-guided U-Net for breast lesion segmentation in ultrasound computed tomography images.Medical & biological engineering & computing · 2026Article
- Diagnostic accuracy of machine-learning-based image analysis for early detection of Kaposi sarcoma in people living with HIV.Frontiers in artificial intelligence · 2026Review
- YOLO AI model based on an automated breast volume scanner for the detection of benign and malignant breast lesions.Quantitative imaging in medicine and surgery · 2025Article
- A Cross-scale Attention-Based U-Net for Breast Ultrasound Image Segmentation.Journal of imaging informatics in medicine · 2025Article
- Attention gate and dilation U-shaped network (GDUNet): an efficient breast ultrasound image segmentation network with multiscale information extraction.Quantitative imaging in medicine and surgery · 2024Article
- An efficient deep learning strategy for accurate and automated detection of breast tumors in ultrasound image datasets.Frontiers in oncology · 2024Article
- Design of a novel intelligent computing framework for predictive solutions of malaria propagation model.PloS one · 2024Article
- Automated breast tumor ultrasound image segmentation with hybrid UNet and classification using fine-tuned CNN model.Heliyon · 2023Article
- Improving Breast Cancer Detection and Diagnosis through Semantic Segmentation Using the Unet3+ Deep Learning Framework.Biomedicines · 2023Article
- Breast Cancer Classification Depends on the Dynamic Dipper Throated Optimization Algorithm.Biomimetics (Basel, Switzerland) · 2023Article
- A Survey of Convolutional Neural Network in Breast Cancer.Computer modeling in engineering & sciences : CMES · 2023Article
- Efficient Breast Cancer Diagnosis from Complex Mammographic Images Using Deep Convolutional Neural Network.Computational intelligence and neuroscience · 2023Article
- Automated Detection of Corneal Ulcer Using Combination Image Processing and Deep Learning.Diagnostics (Basel, Switzerland) · 2022Article
- Efficient System for Delimitation of Benign and Malignant Breast Masses.Entropy (Basel, Switzerland) · 2022Article
- Breast Cancer Classification by Using Multi-Headed Convolutional Neural Network Modeling.Healthcare (Basel, Switzerland) · 2022Article
- Semantic Segmentation of the Malignant Breast Imaging Reporting and Data System Lexicon on Breast Ultrasound Images by Using DeepLab v3.Sensors (Basel, Switzerland) · 2022Article
- A Multi-Agent Deep Reinforcement Learning Approach for Enhancement of COVID-19 CT Image Segmentation.Journal of personalized medicine · 2022Article
- Breast Cancer Classification from Ultrasound Images Using Probability-Based Optimal Deep Learning Feature Fusion.Sensors (Basel, Switzerland) · 2022Article
- A High-Precision Classification Method of Mammary Cancer Based on Improved DenseNet Driven by an Attention Mechanism.Computational and mathematical methods in medicine · 2022Article
- Research on Classification Method of Medical Ultrasound Image Processing Based on Neural Network.Computational intelligence and neuroscience · 2022Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 3 countries.
Funding
Abstract
Breast cancer is becoming more dangerous by the day. The death rate in developing countries is rapidly increasing. As a result, early detection of breast cancer is critical, leading to a lower death rate. Several researchers have worked on breast cancer segmentation and classification using various imaging modalities. The ultrasonic imaging modality is one of the most cost-effective imaging techniques, with a higher sensitivity for diagnosis. The proposed study segments ultrasonic breast lesion images using a Dilated Semantic Segmentation Network (Di-CNN) combined with a morphological erosion operation. For feature extraction, we used the deep neural network DenseNet201 with transfer learning. We propose a 24-layer CNN that uses transfer learning-based feature extraction to further validate and ensure the enriched features with target intensity. To classify the nodules, the feature vectors obtained from DenseNet201 and the 24-layer CNN were fused using parallel fusion. The proposed methods were evaluated using a 10-fold cross-validation on various vector combinations. The accuracy of CNN-activated feature vectors and DenseNet201-activated feature vectors combined with the Support Vector Machine (SVM) classifier was 90.11 percent and 98.45 percent, respectively. With 98.9 percent accuracy, the fused version of the feature vector with SVM outperformed other algorithms. When compared to recent algorithms, the proposed algorithm achieves a better breast cancer diagnosis rate.
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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.