ArticleScientific reports2024
Intelligent breast cancer diagnosis with two-stage using mammogram images.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A novel hierarchical transfer learning to enhance breast cancer detection on screening mammography using deep learning.Physical and engineering sciences in medicine · 2026Article
- ViT-FuseNet: Same-Patient MRI-Pathology Feature Fusion for Multimodal Breast Cancer Diagnosis.Journal of clinical medicine · 2026Article
- MedGAN-SSM: Multimodal brain image synthesis network integration using SSM empowered GAN.iScience · 2026Article
- Hybrid MICO-LAC Segmentation with Panoptic Tumor Instance Analysis for Dense Breast Mammograms.Journal of imaging · 2026Article
- Brain tumor classification using optimized ResNet50 with dynamic precision optimization for enhanced speed and diagnostic accuracy.Scientific reports · 2026Article
- HSSAM-Net: hyper-scale shifted aggregation network for precise colorectal polyp segmentation in endoscopic images.Scientific reports · 2025Article
- DVF-YOLO-Seg: A two-stage breast mass segmentation model with enhanced feature extraction and small lesion detection.Digital healthArticle
- Deep Learning-Based Analysis of Mammographic Images for Breast Cancer Detection Using Transfer Learning.Healthcare technology lettersArticle
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer (BC) significantly contributes to cancer-related mortality in women, underscoring the criticality of early detection for optimal patient outcomes. Mammography is a key tool for identifying and diagnosing breast abnormalities; however, accurately distinguishing malignant mass lesions remains challenging. To address this issue, we propose a novel deep learning approach for BC screening utilizing mammography images. Our proposed model comprises three distinct stages: data collection from established benchmark sources, image segmentation employing an Atrous Convolution-based Attentive and Adaptive Trans-Res-UNet (ACA-ATRUNet) architecture, and BC identification via an Atrous Convolution-based Attentive and Adaptive Multi-scale DenseNet (ACA-AMDN) model. The hyperparameters within the ACA-ATRUNet and ACA-AMDN models are optimized using the Modified Mussel Length-based Eurasian Oystercatcher Optimization (MML-EOO) algorithm. The performance is evaluated using a variety of metrics, and a comparative analysis against conventional methods is presented. Our experimental results reveal that the proposed BC detection framework attains superior precision rates in early disease detection, demonstrating its potential to enhance mammography-based screening methodologies.
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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.