Table 1 Some recent works for breast cancer classification

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