ArticleInternational journal of general medicine2022
Artificial Intelligence-Based Breast Cancer Diagnosis Using Ultrasound Images and Grid-Based Deep Feature Generator.
Article in International journal of general medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- A review of application of Artificial Intelligence in breast cancer detection and treatment.Discover oncology · 2026Review
- Vision Transformers-Based Deep Feature Generation Framework for Hydatid Cyst Classification in Computed Tomography Images.Journal of imaging informatics in medicine · 2026Article
- BrCaM an artificial intelligence model for surgical decision making in breast cancer.Scientific reports · 2026Article
- Artificial intelligence for breast cancer management.Communications medicine · 2026Review
- The role of AI for improved management of breast cancer: Enhanced diagnosis and health disparity mitigation.Computer methods and programs in biomedicine · 2025Review
- Multi-Class Classification of Breast Ultrasound Images Using Vision Transformer-Based Ensemble Learning.Diagnostics (Basel, Switzerland) · 2025Article
- Machine learning can reliably predict malignancy of breast lesions based on clinical and ultrasonographic features.Breast cancer research and treatment · 2025Article
- Untangling Breast Cancer: Trailing Towards Nanoformulations-based Drug Development.Recent patents on nanotechnology · 2025Review
- Innovative breast cancer detection using a segmentation-guided ensemble classification framework.Biomedical engineering letters · 2025Article
- The Impact of Artificial Intelligence on Cancer Diagnosis and Treatment: A Review.Cancer informatics · 2025Review
- Deep Learning for Describing Breast Ultrasound Images with BI-RADS Terms.Journal of imaging informatics in medicine · 2024Article
- Use of ultrasound imaging Omics in predicting molecular typing and assessing the risk of postoperative recurrence in breast cancer.BMC women's health · 2024Article
- Artificial intelligence in breast cancer: application and future perspectives.Journal of cancer research and clinical oncology · 2023Review
- 5G-Based Telerobotic Ultrasound System Improves Access to Breast Examination in Rural and Remote Areas: A Prospective and Two-Scenario Study.Diagnostics (Basel, Switzerland) · 2023Article
- The Systematic Review of Artificial Intelligence Applications in Breast Cancer Diagnosis.Diagnostics (Basel, Switzerland) · 2022Review
- Deep Learning-Based Artificial Intelligence to Investigate Targeted Nanoparticles' Uptake in TNBC Cells.International journal of molecular sciences · 2022Article
- A machine learning model based on ultrasound image features to assess the risk of sentinel lymph node metastasis in breast cancer patients: Applications of scikit-learn and SHAP.Frontiers in oncology · 2022Article
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9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Purpose: Breast cancer is a prominent cancer type with high mortality. Early detection of breast cancer could serve to improve clinical outcomes. Ultrasonography is a digital imaging technique used to differentiate benign and malignant tumors. Several artificial intelligence techniques have been suggested in the literature for breast cancer detection using breast ultrasonography (BUS). Nowadays, particularly deep learning methods have been applied to biomedical images to achieve high classification performances. Patients and Methods: This work presents a new deep feature generation technique for breast cancer detection using BUS images. The widely known 16 pre-trained CNN models have been used in this framework as feature generators. In the feature generation phase, the used input image is divided into rows and columns, and these deep feature generators (pre-trained models) have applied to each row and column. Therefore, this method is called a grid-based deep feature generator. The proposed grid-based deep feature generator can calculate the error value of each deep feature generator, and then it selects the best three feature vectors as a final feature vector. In the feature selection phase, iterative neighborhood component analysis (INCA) chooses 980 features as an optimal number of features. Finally, these features are classified by using a deep neural network (DNN). Results: The developed grid-based deep feature generation-based image classification model reached 97.18% classification accuracy on the ultrasonic images for three classes, namely malignant, benign, and normal. Conclusion: The findings obviously denoted that the proposed grid deep feature generator and INCA-based feature selection model successfully classified breast ultrasonic images.
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