ArticleTomography (Ann Arbor, Mich.)2022
Improving Performance of Breast Lesion Classification Using a ResNet50 Model Optimized with a Novel Attention Mechanism.
Article in Tomography (Ann Arbor, Mich.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Leveraging deep learning and explainable AI for effective liver tumor classification from CT scan images.Frontiers in oncology · 2026Article
- An attention-based transfer learning framework for breast cancer classification in mammography under limited data conditions.Frontiers in medical technology · 2026Article
- Multimodal deep learning for breast tumor classification: Integrating mammography and ultrasound for enhanced diagnostic accuracy.Journal of applied clinical medical physics · 2026Article
- Combination Ensemble and Explainable Deep Learning Framework for High-Accuracy Classification of Wild Edible Macrofungi.Biology · 2025Article
- Ovarian masses suggested for MRI examination: assessment of deep learning models based on non-contrast-enhanced MRI sequences for predicting malignancy.Abdominal radiology (New York) · 2025Article
- Hyperspectral Imaging for Enhanced Skin Cancer Classification Using Machine Learning.Bioengineering (Basel, Switzerland) · 2025Article
- Image Fusion and Target Detection Based on Dual ResNet for Power Sensing Equipment.Sensors (Basel, Switzerland) · 2025Article
- An interpretable framework for gastric cancer classification using multi-channel attention mechanisms and transfer learning approach on histopathology images.Scientific reports · 2025Article
- Feasibility study of AI-assisted multi-parameter MRI diagnosis of prostate cancer.Scientific reports · 2025Article
- Physical and mental health management for the older adult using XGBoost algorithm supported by new media technology: developing personalized health intervention plans using healthcare data from the CLHLS database.Frontiers in public health · 2025Article
- Skin lesion segmentation using deep learning algorithm with ant colony optimization.BMC medical informatics and decision making · 2024Article
- Exploiting histopathological imaging for early detection of lung and colon cancer via ensemble deep learning model.Scientific reports · 2024Article
- Differentiation of granulomatous nodules with lobulation and spiculation signs from solid lung adenocarcinomas using a CT deep learning model.BMC cancer · 2024Article
- A Neoteric Feature Extraction Technique to Predict the Survival of Gastric Cancer Patients.Diagnostics (Basel, Switzerland) · 2024Article
- Machine learning and new insights for breast cancer diagnosis.The Journal of international medical research · 2024Review
- Multiparametric MRI-based radiomics approach with deep transfer learning for preoperative prediction of Ki-67 status in sinonasal squamous cell carcinoma.Frontiers in oncology · 2024Article
- A multi-stage fusion framework to classify breast lesions using deep learning and radiomics features computed from four-view mammograms.Medical physics · 2023Article
- Automated Quantification of Pneumonia Infected Volume in Lung CT Images: A Comparison with Subjective Assessment of Radiologists.Bioengineering (Basel, Switzerland) · 2023Article
- Ischemic stroke subtyping method combining convolutional neural network and radiomics.Journal of X-ray science and technology · 2023Article
- Distinguishing bronchoscopically observed anatomical positions of airway under by convolutional neural network.Therapeutic advances in chronic disease · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Background: The accurate classification between malignant and benign breast lesions detected on mammograms is a crucial but difficult challenge for reducing false-positive recall rates and improving the efficacy of breast cancer screening. Objective: This study aims to optimize a new deep transfer learning model by implementing a novel attention mechanism in order to improve the accuracy of breast lesion classification. Methods: ResNet50 is selected as the base model to develop a new deep transfer learning model. To enhance the accuracy of breast lesion classification, we propose adding a convolutional block attention module (CBAM) to the standard ResNet50 model and optimizing a new model for this task. We assembled a large dataset with 4280 mammograms depicting suspicious soft-tissue mass-type lesions. A region of interest (ROI) is extracted from each image based on lesion center. Among them, 2480 and 1800 ROIs depict verified benign and malignant lesions, respectively. The image dataset is randomly split into two subsets with a ratio of 9:1 five times to train and test two ResNet50 models with and without using CBAM. Results: Using the area under ROC curve (AUC) as an evaluation index, the new CBAM-based ResNet50 model yields AUC = 0.866 ± 0.015, which is significantly higher than that obtained by the standard ResNet50 model (AUC = 0.772 ± 0.008) (p < 0.01). Conclusion: This study demonstrates that although deep transfer learning technology attracted broad research interest in medical-imaging informatic fields, adding a new attention mechanism to optimize deep transfer learning models for specific application tasks can play an important role in further improving model performances.
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