ArticleScientific reports2022
ResNet-32 and FastAI for diagnoses of ductal carcinoma from 2D tissue slides.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
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- Development of a multi-fusion convolutional neural network (MF-CNN) for enhanced gastrointestinal disease diagnosis in endoscopy image analysis.PeerJ. Computer science · 2024Article
- Real-Time Ultrasound-Computed Tomography Fusion with Volume Navigation to Assess Pancreatic Cystic Lesions.Current oncology (Toronto, Ont.) · 2023Article
- Improving Structural MRI Preprocessing with Hybrid Transformer GANs.Life (Basel, Switzerland) · 2023Article
- Intelligent diagnostic model for malaria parasite detection and classification using imperative inception-based capsule neural networks.Scientific reports · 2023Article
- Machine Learning Approaches for Assessing Risk Factors of Adrenal Insufficiency in Patients Undergoing Immune Checkpoint Inhibitor Therapy.Pharmaceuticals (Basel, Switzerland) · 2023Article
- Neural network-based prognostic predictive tool for gastric cardiac cancer: the worldwide retrospective study.BioData mining · 2023Article
- Improved EfficientNet for corn disease identification.Frontiers in plant science · 2023Article
- TBC-YOLOv7: a refined YOLOv7-based algorithm for tea bud grading detection.Frontiers in plant science · 2023Article
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5 authors.
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Abstract
Carcinoma is a primary source of morbidity in women globally, with metastatic disease accounting for most deaths. Its early discovery and diagnosis may significantly increase the odds of survival. Breast cancer imaging is critical for early identification, clinical staging, management choices, and treatment planning. In the current study, the FastAI technology is used with the ResNet-32 model to precisely identify ductal carcinoma. ResNet-32 is having few layers comparted to majority of its counterparts with almost identical performance. FastAI offers a rapid approximation toward the outcome for deep learning models via GPU acceleration and a faster callback mechanism, which would result in faster execution of the model with lesser code and yield better precision in classifying the tissue slides. Residual Network (ResNet) is proven to handle the vanishing gradient and effective feature learning better. Integration of two computationally efficient technologies has yielded a precision accuracy with reasonable computational efforts. The proposed model has shown considerable efficiency in the evaluating parameters like sensitivity, specificity, accuracy, and F1 Score against the other dominantly used deep learning models. These insights have shown that the proposed approach might assist practitioners in analyzing Breast Cancer (BC) cases appropriately, perhaps saving future complications and death. Clinical and pathological analysis and predictive accuracy have been improved with digital image processing.
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