Portal Penelitian CNN Breast Cancer

No	Type BC	Dataset	Model	Overview of CNN Blocks	Result	Author
11	Mitotic cell detection	ICPR 2012 Mitosis Dataset, ICPR 2014 Mitosis Dataset, AMIDA13 Dataset	Fully Convolutional Networks (FCNs)	Not reported	On the 2014 ICPR mitosis dataset, the model achieved an F-score of 0.575, while on the AMIDA13 dataset, it obtained an F-score of 0.698.	PartMitosis: A Partially Supervised Deep Learning Framework for Mitosis Detection in Breast Cancer Histopathology Images
12	Mass and Microcalcifications	Mammographic Image Analysis Society (MIAS)	A hybrid classifier combining Extreme Learning Machine (ELM) with Glowworm Swarm Optimization (GSO) and Fruit Fly Optimization Algorithm (FOA), hereafter denoted GSO-ELM-FOA, was developed.	Reported	Acc=0.9915, Prec=1, Sens=0.9791	Detection and classification of breast cancer from digital mammograms using hybrid extreme learning machine classifier
13	Invasive Ductal Carcinoma (IDC)	Wisconsin breast cancer (WBC)	Optimized Stacking Ensemble Learning (OSEL) model	Not reported	Acc=0.9945, Pre=0.9900, Rec=0.9800, F-measure=0.9900,	Optimized Stacking Ensemble Learning Model for Breast Cancer Detection and Classification Using Machine Learning
14	Malign Breast Cancer	Microwave Scattering Dataset from Breast Phantoms for Tumor Detection comprises calibrated experimental measurement data on gel-like phantoms mimicking human tissue, tested at the ERMIAS Laboratory, University of Calabria.	A convolutional neural network (CNN) was utilized to estimate dielectric properties (relative permittivity and conductivity) from electromagnetic measurements of breast phantoms, while the quadratic programming-based BIM framework was applied to solve the	Reported	The combined CNN and quadratic BIM method achieved over 90% accuracy in reconstructing relative permittivity and conductivity within ~15 minutes. Compared to the standard quadratic BIM with error rates of 44?89%, the hybrid approach reduced errors to below 10%.	Machine Learning Approach to Quadratic Programming-Based Microwave Imaging for Breast Cancer Detection
15	Malign Breast Cancer	The Cancer Genome Atlas (TCGA) dan Genomic Data Commons (GDC) Data Portal	Machine learning models such as RF, SVM, NB, KNN, DT, LR, ANN, and deep learning (DL) represent the primary methods frequently employed for breast cancer classification and detection using miRNA expression data in the reviewed studies.	Not reported	Based on the reviewed studies, Deep Learning (DL) and Artificial Neural Networks (ANN) achieved the highest performance in breast cancer classification and detection using miRNA biomarkers, both reporting accuracies up to 100%, while other methods such as DT (99.12%), KNN (99.2%), RF (AUC 99.5?99.9%), and SVM (AUC 93.8?99.6%) showed strong but slightly lower results.	Addressing the Clinical Feasibility of Adopting Circulating miRNA for Breast Cancer Detection, Monitoring and Management with Artificial Intelligence and Machine Learning Platforms
16	Malign Breast Cancer	Numerical Breast Phantom Repository milik University of Wisconsin Cross-Disciplinary Electromagnetics Laboratory (UWCEM)	The Born Iterative Method (BIM) combined with quadratic programming and a U-Net-based convolutional neural network (CNN).	Not reported	Acc=0.9540 with an average relative error of 2.4%, the quadratic BIM reconstruction required less than 5 minutes per image, while the trained CNN reduced the reconstruction time to under 5 seconds per image	Enhanced Machine Learning Approach for Accurate and Fast Resolution of Inverse Scattering Problem in Breast Cancer Detection
17	Breast Pathology	Wisconsin breast cancer (WBC)	This study conducted a comparative analysis using Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Logistic Regression (LR), AdaBoost, Decision Tree, and Random Forest.	Not reported	The study demonstrated that Support Vector Machine (SVM) achieved the best performance, reaching up to 0.977 accuracy with AUC 0.99, outperforming other machine learning models in breast cancer classification.	Bio-Imaging-Based Machine Learning Algorithm for Breast Cancer Detection
18	Breast Pathology	Wisconsin breast cancer (WBC)	Deep features extracted via CNN were classified using a voting ensemble combining Logistic Regression and Stochastic Gradient Descent.	Not reported	LR+SGD: Acc=1, Prec=1, Rec=1, F1-score=1	Breast Cancer Detection Using Convoluted Features and Ensemble Machine Learning Algorithm
19	Invasive Ductal Carcinoma (IDC)	CAMELYON16, CAMELYON17, LocalSentinel (161 slides), LocalAxillary (57 slides), and LocalNegativeAxillary (259 slides).	This study utilized a modified DenseNet architecture, retrained with local datasets (LocalSentinel and LocalAxillary) in addition to CAMELYON data, to enhance generalization for whole-slide image analysis.	Not reported	The deep learning model achieved high performance on CAMELYON16: AUC=0.969, FROC=0.838, but declined on LocalSentinel: AUC=0.929, FROC=0.744) and LocalAxillary (FROC=0.503), while retraining with local data improved performance by ~4% on LocalSentinel and up to 11% (AUC) and 49% (FROC) on LocalAxillary.	Generalization of Deep Learning in Digital Pathology Experience in Breast Cancer Metastasis Detection
20	Breast Pathology	CBIS-DDSM (Curated Breast Imaging Subset of Digital Database for Screening Mammography) and The Cancer Imaging Archive (TCIA), Kaggle	The ensemble machine learning models included Random Forest (RF), eXtreme Gradient Boosting (XGB), Decision Tree (DT), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN).	Not reported	From these model combinations, several ensemble models were developed, with the best being the RF-XGB-10, which utilized 10 selected features based on the Random Forest feature importance algorithm. This model achieved a testing Acc=0.9805, MCC=0.9727% F1-Score=0.9805, AUC=0.9891	An Effective Ensemble Machine Learning Approach to Classify Breast Cancer Based on Feature Selection and Lesion Segmentation Using Preprocessed Mammograms

Portal Penelitian CNN Breast Cancer

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