ArticleComputational intelligence and neuroscience2022
Predicting Breast Cancer Based on Optimized Deep Learning Approach.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 59 citations in OpenAlex.
- Diagnostic accuracy of machine learning-based magnetic resonance imaging models in breast cancer classification: a systematic review and meta-analysis.World journal of surgical oncology · 2025Pooled it
- The Danish Chronic Disease Cohort: using digital footprints to identify chronic disease patterns.European journal of epidemiology · 2026Article
- A multi stage deep learning model for accurate segmentation and classification of breast lesions in mammography.Scientific reports · 2025Article
- Enhanced metastasis risk prediction in cutaneous squamous cell carcinoma using deep learning and computational histopathology.NPJ precision oncology · 2025Article
- Breast Tumor Detection and Diagnosis Using an Improved Faster R-CNN in DCE-MRI.Bioengineering (Basel, Switzerland) · 2024Article
- Deep learning applications in breast cancer histopathological imaging: diagnosis, treatment, and prognosis.Breast cancer research : BCR · 2024Review
- Machine learning to predict the early recurrence of intrahepatic cholangiocarcinoma: A systematic review and meta‑analysis.Oncology letters · 2024Article
- A machine learning and deep learning-based integrated multi-omics technique for leukemia prediction.Heliyon · 2024Article
- Development and validation of predictive model based on deep learning method for classification of dyslipidemia in Chinese medicine.Health information science and systems · 2023Article
- A review of genetic variant databases and machine learning tools for predicting the pathogenicity of breast cancer.Briefings in bioinformatics · 2023Review
- HistoMIL: A Python package for training multiple instance learning models on histopathology slides.iScience · 2023Article
- Automated Diagnosis for Colon Cancer Diseases Using Stacking Transformer Models and Explainable Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2023Article
- Ensemble Learning Based on Hybrid Deep Learning Model for Heart Disease Early Prediction.Diagnostics (Basel, Switzerland) · 2022Article
- ResNet-32 and FastAI for diagnoses of ductal carcinoma from 2D tissue slides.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is a dangerous disease with a high morbidity and mortality rate. One of the most important aspects in breast cancer treatment is getting an accurate diagnosis. Machine-learning (ML) and deep learning techniques can help doctors in making diagnosis decisions. This paper proposed the optimized deep recurrent neural network (RNN) model based on RNN and the Keras-Tuner optimization technique for breast cancer diagnosis. The optimized deep RNN consists of the input layer, five hidden layers, five dropout layers, and the output layer. In each hidden layer, we optimized the number of neurons and rate values of the dropout layer. Three feature-selection methods have been used to select the most important features from the database. Five regular ML models, namely decision tree (DT), support vector machine (SVM), random forest (RF), naive Bayes (NB), and
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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.