ArticlePloS one2021
Automatic semantic segmentation of breast tumors in ultrasound images based on combining fuzzy logic and deep learning-A feasibility study.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning Techniques, Applications, and Potential Future Opportunities in Pressure Injuries (Bedsores) Management: A Systematic Review.International journal of environmental research and public health · 2023Pooled it
- Ensemble-based high-performance deep learning models for medical image retrieval in breast cancer detection.Scientific reports · 2026Article
- Deep Learning for Tumor Segmentation and Multiclass Classification in Breast Ultrasound Images Using Pretrained Models.Sensors (Basel, Switzerland) · 2025Article
- The study of dual-phaseScientific reports · 2025Article
- A deep learning model based on chest CT to predict benign and malignant breast masses and axillary lymph node metastasis.Biomolecules & biomedicine · 2025Article
- AI-Powered Clinical Decision Support Systems in Disease Diagnosis, Treatment Planning, and Prognosis: A Systematic Review.Medical journal of the Islamic Republic of Iran · 2025Review
- Charting new territories: fuzzy systems in English language teaching and learning.PeerJ. Computer science · 2025Article
- Optimizing breast cancer ultrasound diagnosis: a comparative study of AI model performance and image resolution.Frontiers in oncology · 2025Article
- State-of-the-Art of Breast Cancer Diagnosis in Medical Images via Convolutional Neural Networks (CNNs).Journal of healthcare informatics research · 2023Review
- Automatic segmentation of inconstant fractured fragments for tibia/fibula from CT images using deep learning.Scientific reports · 2023Article
- A Modified LeNet CNN for Breast Cancer Diagnosis in Ultrasound Images.Diagnostics (Basel, Switzerland) · 2023Article
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- Breast Cancer Classification Depends on the Dynamic Dipper Throated Optimization Algorithm.Biomimetics (Basel, Switzerland) · 2023Article
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- Establishment of a deep-learning system to diagnose BI-RADS4a or higher using breast ultrasound for clinical application.Cancer science · 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
- Article
- Breast Cancer Classification from Ultrasound Images Using Probability-Based Optimal Deep Learning Feature Fusion.Sensors (Basel, Switzerland) · 2022Article
- A Novel Multistage Transfer Learning for Ultrasound Breast Cancer Image Classification.Diagnostics (Basel, Switzerland) · 2022Article
- A Novel CNN pooling layer for breast cancer segmentation and classification from thermograms.PloS one · 2022Article
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6 authors.
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Abstract
Computer aided diagnosis (CAD) of biomedical images assists physicians for a fast facilitated tissue characterization. A scheme based on combining fuzzy logic (FL) and deep learning (DL) for automatic semantic segmentation (SS) of tumors in breast ultrasound (BUS) images is proposed. The proposed scheme consists of two steps: the first is a FL based preprocessing, and the second is a Convolutional neural network (CNN) based SS. Eight well-known CNN based SS models have been utilized in the study. Studying the scheme was by a dataset of 400 cancerous BUS images and their corresponding 400 ground truth images. SS process has been applied in two modes: batch and one by one image processing. Three quantitative performance evaluation metrics have been utilized: global accuracy (GA), mean Jaccard Index (mean intersection over union (IoU)), and mean BF (Boundary F1) Score. In the batch processing mode: quantitative metrics' average results over the eight utilized CNNs based SS models over the 400 cancerous BUS images were: 95.45% GA instead of 86.08% without applying fuzzy preprocessing step, 78.70% mean IoU instead of 49.61%, and 68.08% mean BF score instead of 42.63%. Moreover, the resulted segmented images could show tumors' regions more accurate than with only CNN based SS. While, in one by one image processing mode: there has been no enhancement neither qualitatively nor quantitatively. So, only when a batch processing is needed, utilizing the proposed scheme may be helpful in enhancing automatic ss of tumors in BUS images. Otherwise applying the proposed approach on a one-by-one image mode will disrupt segmentation's efficiency. The proposed batch processing scheme may be generalized for an enhanced CNN based SS of a targeted region of interest (ROI) in any batch of digital images. A modified small dataset is available: https://www.kaggle.com/mohammedtgadallah/mt-small-dataset (S1 Data).
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