ArticleSensors (Basel, Switzerland)2023
Rethinking Breast Cancer Diagnosis through Deep Learning Based Image Recognition.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Research on multi-stage deep learning based intelligent diagnosis of skin diseases and skin medicine diagnosis community construction concept.Scientific reports · 2026Article
- Predicting prognosis of patients with hepatitis B virus-related acute-on-chronic liver failure from longitudinal ultrasound images using a multi-task deep learning approach.Annals of medicine · 2025Article
- The role of AI for improved management of breast cancer: Enhanced diagnosis and health disparity mitigation.Computer methods and programs in biomedicine · 2025Review
- Development and validation of a semi-automatic radiomics ensemble model for preoperative evaluation of breast masses in mammotome-assisted minimally invasive resection.Gland surgery · 2025Article
- Automatic joint segmentation and classification of breast ultrasound images via multi-task learning with object contextual attention.Frontiers in oncology · 2025Article
- Application of image recognition technology in pathological diagnosis of blood smears.Clinical and experimental medicine · 2024Review
- DI-UNet: dual-branch interactive U-Net for skin cancer image segmentation.Journal of cancer research and clinical oncology · 2023Article
- Breast Cancer Classification Using Synthesized Deep Learning Model with Metaheuristic Optimization Algorithm.Diagnostics (Basel, Switzerland) · 2023Article
- Differentiation of Low and High-Grade Meningiomas Using Transfer Learning on MR Images.Healthcare technology lettersArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
This paper explored techniques for diagnosing breast cancer using deep learning based medical image recognition. X-ray (Mammography) images, ultrasound images, and histopathology images are used to improve the accuracy of the process by diagnosing breast cancer classification and by inferring their affected location. For this goal, the image recognition application strategies for the maximal diagnosis accuracy in each medical image data are investigated in terms of various image classification (VGGNet19, ResNet50, DenseNet121, EfficietNet v2), image segmentation (UNet, ResUNet++, DeepLab v3), and related loss functions (binary cross entropy, dice Loss, Tversky loss), and data augmentation. As a result of evaluations through the presented methods, when using filter-based data augmentation, ResNet50 showed the best performance in image classification, and UNet showed the best performance in both X-ray image and ultrasound image as image segmentation. When applying the proposed image recognition strategies for the maximal diagnosis accuracy in each medical image data, the accuracy can be improved by 33.3% in image segmentation in X-ray images, 29.9% in image segmentation in ultrasound images, and 22.8% in image classification in histopathology images.
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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.