ArticleSensors (Basel, Switzerland)2022
Fine-Tuned DenseNet-169 for Breast Cancer Metastasis Prediction Using FastAI and 1-Cycle Policy.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers.
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Who cites it
57 citing papers in PubMed.
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- An explainable AI-driven deep neural network for accurate breast cancer detection from histopathological and ultrasound images.Scientific reports · 2025Article
- Exploring the complex nature of implementation of Artificial intelligence in clinical practice: an interview study with healthcare professionals, researchers and Policy and Governance Experts.PLOS digital health · 2025Article
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- Ethical and regulatory challenges of AI technologies in healthcare: A narrative review.Heliyon · 2024Review
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- Pneumonia detection on chest X-rays from Xception-based transfer learning and logistic regression.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Article
- Remodeling of tumour microenvironment: strategies to overcome therapeutic resistance and innovate immunoengineering in triple-negative breast cancer.Frontiers in immunology · 2024Review
- Skin Lesion Synthesis and Classification Using an Improved DCGAN Classifier.Diagnostics (Basel, Switzerland) · 2023Article
- Neural network-based prognostic predictive tool for gastric cardiac cancer: the worldwide retrospective study.BioData mining · 2023Article
- Automatic Identification and Segmentation of Orbital Blowout Fractures Based on Artificial Intelligence.Translational vision science & technology · 2023Article
- Alzheimer Disease Classification through Transfer Learning Approach.Diagnostics (Basel, Switzerland) · 2023Article
- Explainable AI for Estimating Pathogenicity of Genetic Variants Using Large-Scale Knowledge Graphs.Cancers · 2023Article
- Multimodal Deep Learning-Based Prognostication in Glioma Patients: A Systematic Review.Cancers · 2023Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Lymph node metastasis in breast cancer may be accurately predicted using a DenseNet-169 model. However, the current system for identifying metastases in a lymph node is manual and tedious. A pathologist well-versed with the process of detection and characterization of lymph nodes goes through hours investigating histological slides. Furthermore, because of the massive size of most whole-slide images (WSI), it is wise to divide a slide into batches of small image patches and apply methods independently on each patch. The present work introduces a novel method for the automated diagnosis and detection of metastases from whole slide images using the Fast AI framework and the 1-cycle policy. Additionally, it compares this new approach to previous methods. The proposed model has surpassed other state-of-art methods with more than 97.4% accuracy. In addition, a mobile application is developed for prompt and quick response. It collects user information and models to diagnose metastases present in the early stages of cancer. These results indicate that the suggested model may assist general practitioners in accurately analyzing breast cancer situations, hence preventing future complications and mortality. With digital image processing, histopathologic interpretation and diagnostic accuracy have improved considerably.
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