ArticleMultimedia tools and applications2022
Breast cancer diagnosis in an early stage using novel deep learning with hybrid optimization technique.
Article in Multimedia tools and applications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- A causal multimodal framework for privacy-preserving early-stage cancer detection and adaptive testing.Scientific reports · 2026Article
- A hybrid CNN-XLSTM-GRU deep learning model with autoencoder-based feature selection for hypothyroidism diagnosis.PloS one · 2026Article
- Breast cancer detection using optimized hyperbolic graph attention with bidirectional convolutional neural network.Discover oncology · 2025Article
- Cancer and Aging Biomarkers: Classification, Early Detection Technologies and Emerging Research Trends.Biosensors · 2025Review
- Breast cancer classification based on hybrid CNN with LSTM model.Scientific reports · 2025Article
- Extracting Knowledge from Machine Learning Models to Diagnose Breast Cancer.Life (Basel, Switzerland) · 2025Article
- A depth analysis of recent innovations in non-invasive techniques using artificial intelligence approach for cancer prediction.Medical & biological engineering & computing · 2024Review
- RNA-Seq analysis for breast cancer detection: a study on paired tissue samples using hybrid optimization and deep learning techniques.Journal of cancer research and clinical oncology · 2024Article
- Fine tuning deep learning models for breast tumor classification.Scientific reports · 2024Article
- Revolutionizing Breast Cancer Diagnosis: A Concatenated Precision through Transfer Learning in Histopathological Data Analysis.Diagnostics (Basel, Switzerland) · 2024Article
- Self-attention random forest for breast cancer image classification.Frontiers in oncology · 2023Article
- Classification of breast cancer using a manta-ray foraging optimized transfer learning framework.PeerJ. Computer science · 2022Article
- Breast Cancer Detection and Classification Empowered With Transfer Learning.Frontiers in public health · 2022Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is one of the primary causes of death that is occurred in females around the world. So, the recognition and categorization of initial phase breast cancer are necessary to help the patients to have suitable action. However, mammography images provide very low sensitivity and efficiency while detecting breast cancer. Moreover, Magnetic Resonance Imaging (MRI) provides high sensitivity than mammography for predicting breast cancer. In this research, a novel Back Propagation Boosting Recurrent Wienmed model (BPBRW) with Hybrid Krill Herd African Buffalo Optimization (HKH-ABO) mechanism is developed for detecting breast cancer in an earlier stage using breast MRI images. Initially, the MRI breast images are trained to the system, and an innovative Wienmed filter is established for preprocessing the MRI noisy image content. Moreover, the projected BPBRW with HKH-ABO mechanism categorizes the breast cancer tumor as benign and malignant. Additionally, this model is simulated using Python, and the performance of the current research work is evaluated with prevailing works. Hence, the comparative graph shows that the current research model produces improved accuracy of 99.6% with a 0.12% lower error rate.
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