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Reference | | Method | Advantages | Disadvantages |
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Rouhi et al. [69] | 2015 | Regional growth with a cellular neural network with a specific threshold | Ability to diagnose benign and malignant tumors, high accuracy in classification | High computational complexity (i) Lack of accurate detection of areas with tumor (ii) Lack of comparison with previous methods |
Kaymak et al. [70] | 2017 | Back-Propagation (BP) | A convenient way to use in neural network training, highly fast execution speed in training | Uncertainty of the exact type of approach proposed and lack of comparison at the time of classification and uncertainty of benign and malignant tumors |
Karabatak [71] | 2015 | Naïve Bayesian | Ability to diagnose benign and malignant tumors, high accuracy in classification | High computational complexity (i) Lack of accurate detection of areas with tumor (ii) Lack of comparison with previous methods |
Wang et al. [72] | 2018 | Regression-based methods | Ability to estimate and predict remaining life based on tumor size, high accuracy in detection | |
Pereira et al. [73] | 2014 | Wavelet analysis and genetic algorithm | Ability to diagnose benign and malignant tumors, high accuracy in classification | High computational complexity (i) Lack of accurate diagnosis of areas with tumor (ii) Lack of comparison with previous methods |
Cordeiro et al. [75] | 2016 | Semisupervised adaptive algorithm GrowCut | Ability to diagnose benign and malignant tumors, high accuracy in classification | High computational complexity (i) Lack of accurate detection of areas with tumor (ii) Lack of comparison with previous methods |
Ahmed et al. [76] | 2020 | Mask RCNN | (i) Increased AUC for transfer learning (ii) Use for X-ray mammographic image | (i) Low accuracy (ii) Used low volume dataset for verification (iii) High rate of oversampling |
Lee et al. [77] | 2020 | Multiscale grid average pooling | (i) Utilizing global and local spatial feature (ii) Novel attention module (iii) Ultrasound image dataset | (i) Lower accuracy of segmentation (ii) High computational complexity (iii) A small volume of the dataset |
Soulami et al. [78] | 2021 | UNet model | (i) High accuracy for breast cancer detection (ii) High f1-score and AUC | (i) High complexity model (ii) Overfitting in some models (iii) Lower volume of analysis |
Huang et al. [79] | 2021 | Fuzzy fully CNN | (i) Fuzzy membership function (ii) Conditional random fields | (i) Low sensitivity (ii) Low intersection over union (iii) Low resolution and poor quality |
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