ArticleDiagnostics (Basel, Switzerland)2022
A Novel Multistage Transfer Learning for Ultrasound Breast Cancer Image Classification.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Hypertuned boosting approach with Local Binary Pattern and Pivot Distribution Count method feature extractor for glaucoma identification.Scientific reports · 2026Article
- Development and evaluation of a multistage transfer learning framework for robust medical image analysis.Scientific reports · 2026Article
- Genetic Artificial Intelligence in Gastrointestinal Disease: A Systematic Review.Diagnostics (Basel, Switzerland) · 2025Review
- Nature-Inspired Multi-Level Thresholding Integrated with CNN for Accurate COVID-19 and Lung Disease Classification in Chest X-Ray Images.Diagnostics (Basel, Switzerland) · 2025Article
- HED-Net: a hybrid ensemble deep learning framework for breast ultrasound image classification.Frontiers in artificial intelligence · 2025Article
- Exploring AI Approaches for Breast Cancer Detection and Diagnosis: A Review Article.Breast cancer (Dove Medical Press) · 2025Review
- CNN-Based Cross-Modality Fusion for Enhanced Breast Cancer Detection Using Mammography and Ultrasound.Tomography (Ann Arbor, Mich.) · 2024Article
- Effectiveness of ChatGPT 4.0 in Telemedicine-Based Management of Metastatic Prostate Carcinoma.Diagnostics (Basel, Switzerland) · 2024Article
- The artistic image processing for visual healing in smart city.Scientific reports · 2024Article
- Emerging Biohybrids of Aptamer-Based Nano-Biosensing Technologies for Effective Early Cancer Detection.Molecular diagnosis & therapy · 2024Review
- Unified deep learning models for enhanced lung cancer prediction with ResNet-50-101 and EfficientNet-B3 using DICOM images.BMC medical imaging · 2024Article
- Improving Tumor-Infiltrating Lymphocytes Score Prediction in Breast Cancer with Self-Supervised Learning.Life (Basel, Switzerland) · 2024Article
- Offloading the computational complexity of transfer learning with generic features.PeerJ. Computer science · 2024Article
- State-of-the-Art of Breast Cancer Diagnosis in Medical Images via Convolutional Neural Networks (CNNs).Journal of healthcare informatics research · 2023Review
- Automated breast tumor ultrasound image segmentation with hybrid UNet and classification using fine-tuned CNN model.Heliyon · 2023Article
- Improved Breast Cancer Classification through Combining Transfer Learning and Attention Mechanism.Life (Basel, Switzerland) · 2023Article
- Predicting intensive care need for COVID-19 patients using deep learning on chest radiography.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- A New Deep-Learning-Based Model for Breast Cancer Diagnosis from Medical Images.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
- Classification of Breast Lesions on DCE-MRI Data Using a Fine-Tuned MobileNet.Diagnostics (Basel, Switzerland) · 2023Article
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4 authors.
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Abstract
Breast cancer diagnosis is one of the many areas that has taken advantage of artificial intelligence to achieve better performance, despite the fact that the availability of a large medical image dataset remains a challenge. Transfer learning (TL) is a phenomenon that enables deep learning algorithms to overcome the issue of shortage of training data in constructing an efficient model by transferring knowledge from a given source task to a target task. However, in most cases, ImageNet (natural images) pre-trained models that do not include medical images, are utilized for transfer learning to medical images. Considering the utilization of microscopic cancer cell line images that can be acquired in large amount, we argue that learning from both natural and medical datasets improves performance in ultrasound breast cancer image classification. The proposed multistage transfer learning (MSTL) algorithm was implemented using three pre-trained models: EfficientNetB2, InceptionV3, and ResNet50 with three optimizers: Adam, Adagrad, and stochastic gradient de-scent (SGD). Dataset sizes of 20,400 cancer cell images, 200 ultrasound images from Mendeley and 400 ultrasound images from the MT-Small-Dataset were used. ResNet50-Adagrad-based MSTL achieved a test accuracy of 99 ± 0.612% on the Mendeley dataset and 98.7 ± 1.1% on the MT-Small-Dataset, averaging over 5-fold cross validation. A
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