ArticleDiagnostics (Basel, Switzerland)2022
Bio-Imaging-Based Machine Learning Algorithm for Breast Cancer Detection.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 51 citations in OpenAlex.
- Recent Advances and Emerging Directions in Machine Learning-Based Breast Cancer Drug Discovery: A Comprehensive Review.Breast cancer (Dove Medical Press) · 2026Review
- An optimized transfer learning approach integrating deep convolutional feature extractors for malaria parasite classification in erythrocyte microscopy.Frontiers in medicine · 2025Article
- Usefulness of machine learning and deep learning approaches in screening and early detection of breast cancer.Heliyon · 2023Review
- A Framework of Faster CRNN and VGG16-Enhanced Region Proposal Network for Detection and Grade Classification of Knee RA.Diagnostics (Basel, Switzerland) · 2023Article
- Ensemble Learning-Based Hybrid Segmentation of Mammographic Images for Breast Cancer Risk Prediction Using Fuzzy C-Means and CNN Model.Journal of healthcare engineering · 2023Article
- Deep Transfer Learning-Based Foot No-Ball Detection in Live Cricket Match.Computational intelligence and neuroscience · 2023Article
- Privacy Preserved Cervical Cancer Detection Using Convolutional Neural Networks Applied to Pap Smear Images.Computational and mathematical methods in medicine · 2023Article
- ML-DSTnet: A Novel Hybrid Model for Breast Cancer Diagnosis Improvement Based on Image Processing Using Machine Learning and Dempster-Shafer Theory.Computational intelligence and neuroscience · 2023Article
- FibroVit-Vision transformer-based framework for detection and classification of pulmonary fibrosis from chest CT images.Frontiers in medicine · 2023Article
- Biomedical Text Classification Using Augmented Word Representation Based on Distributional and Relational Contexts.Computational intelligence and neuroscience · 2023Article
- CNN Based Multiclass Brain Tumor Detection Using Medical Imaging.Computational intelligence and neuroscience · 2022Article
- ECG Classification for Detecting ECG Arrhythmia Empowered with Deep Learning Approaches.Computational intelligence and neuroscience · 2022Article
- BrainNet: Optimal Deep Learning Feature Fusion for Brain Tumor Classification.Computational intelligence and neuroscience · 2022Article
- An efficient computer vision-based approach for acute lymphoblastic leukemia prediction.Frontiers in computational neuroscience · 2022Article
- A nomogram for predicting cancer-specific survival and overall survival in elderly patients with nonmetastatic renal cell carcinoma.Frontiers in surgery · 2022Article
- Development and Validation of Embedded Device for Electrocardiogram Arrhythmia Empowered with Transfer Learning.Computational intelligence and neuroscience · 2022Article
- A Novel Hybrid Convolutional Neural Network Approach for the Stomach Intestinal Early Detection Cancer Subtype Classification.Computational intelligence and neuroscience · 2022Article
- Segmentation and Classification of Encephalon Tumor by Applying Improved Fast and Robust FCM Algorithm with PSO-Based ELM Technique.Computational intelligence and neuroscience · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is one of the most widespread diseases in women worldwide. It leads to the second-largest mortality rate in women, especially in European countries. It occurs when malignant lumps that are cancerous start to grow in the breast cells. Accurate and early diagnosis can help in increasing survival rates against this disease. A computer-aided detection (CAD) system is necessary for radiologists to differentiate between normal and abnormal cell growth. This research consists of two parts; the first part involves a brief overview of the different image modalities, using a wide range of research databases to source information such as ultrasound, histography, and mammography to access various publications. The second part evaluates different machine learning techniques used to estimate breast cancer recurrence rates. The first step is to perform preprocessing, including eliminating missing values, data noise, and transformation. The dataset is divided as follows: 60% of the dataset is used for training, and the rest, 40%, is used for testing. We focus on minimizing type one false-positive rate (FPR) and type two false-negative rate (FNR) errors to improve accuracy and sensitivity. Our proposed model uses machine learning techniques such as support vector machine (SVM), logistic regression (LR), and K-nearest neighbor (KNN) to achieve better accuracy in breast cancer classification. Furthermore, we attain the highest accuracy of 97.7% with 0.01 FPR, 0.03 FNR, and an area under the ROC curve (AUC) score of 0.99. The results show that our proposed model successfully classifies breast tumors while overcoming previous research limitations. Finally, we summarize the paper with the future trends and challenges of the classification and segmentation in breast cancer detection.
Indexed as
Identifiers
What OpenQuestion holds
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.