From: Deep transfer learning with fuzzy ensemble approach for the early detection of breast cancer
Works | Methods | Outcomes |
---|---|---|
Mohammed et al. [5] | Logistic, Naïve Bayes (NB), Decision Tree (DT) with Majority voting ensemble approach | Classification prediction: 98.1% accuracy, error rate: 0.01% |
Sannasi et al. [6] | ELM Optimized with an advanced crow-search algorithm (Ensemble approach) | Classification prediction: 98.2%, 97.1%, and 98% accuracies for DDSM, INbreast, and MI-AS databases |
Muhammad et al. [7] | Various Machine Learning Models with Gradient Boosted Ensemble Approach | Classification prediction: 90% accuracy |
Mughal [8] | Back-propagation Neural Model | Classification prediction: 98% accuracy for MIAS and DDSM databases |
Benzheng et al. [9] | CNN architectures with a two-class model | Classification prediction: 97.9% accuracy for histopathological images |
Naresh and Mishra [10] | Logistic and Neural models with Ensemble approach | Classification prediction: 98% accuracy |
Mai Bui and Vinh [11] | Hybrid Deep Learning using VGG16 and VGG19 models | Classification prediction: 98.1% accuracy for histopathological images |
Pratik et al. [12] | Fuzzy concepts with information theory andCoalition game | Classification prediction: 95% accuracy for 4-class problem |
Khan et al. [13] | CNN with transfer learning approaches | Classification prediction: 97.6% accuracy |
Debendra et al. [14] | Deep learning with a 5-learnable layer model | Classification prediction: 96.5% for mammograms and 100% for ultrasound images |