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| | Authors | Method | Objective | Results |
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| 1 | Ribli et al. [27] | Faster R–CNN used mammography to identify tumors and showed that this method was quite time-efficient; however, the faster R–CNN is generally weak, meaning that the training set must contain a large set of ROIs yet complete enough to include all possible waste changes | Diagnosis and classification of masses in breast tissue | The system can detect 90% of malignant lesions in the INbreast dataset with only 0.3 false-positive marks per image |
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| 2 | Gao [28] | They used a low-energy image (LE) similar to full-field digital mammography (FFDM) and a recombination image; in the proposed algorithm, the shallow CNN has the task of “image reconstruction,” and the deep CNN has “extracting features” | Diagnosis and classification of masses in breast tissue | Experimental results on 89 FFDM datasets are obtained using recombinant “virtual” imaging features with an accuracy of 0.90 and AUC = 0.92 |
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| 3 | Jung et al. [29] | The model used a single-stage detector and a two-stage detector; one-step detectors such as RetinaNet generate a fixed number of projections over a network to cover possible positive sample spaces; unlike RetinaNet, the R–CNN mask can classify finite boxes in any range of scales and aspect ratios in any situation by segmenting the pixel surface | Diagnosis and classification of masses in breast tissue | The best result of this algorithm for TPR @ FPPI in the GURO dataset is (0.99 ± [email protected]) |
| (1.00 ± [email protected]) |
| (0.94 ± [email protected]) |
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| 4 | Shams et al. [30] | A deep generative multitasking network (DiaGRAM) has been used to solve data loss and limited training data to interpret the lesions because it is a costly and time-consuming task; this multifunctional network is built on a combination of Concentration neural networks and a generational opposite network | Classification of masses in breast tissue | The results of this algorithm for the INbreast dataset are as follows: |
| Accuracy equal to 9.2% ± 5/93 |
| And AUC = 92.5 ± 2.4% |
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| 5 | Al-masni et al. [31] | A CAD system based on deep learning ROI (area of interest) techniques using CNN called YOLO was developed to diagnose and classify breast masses into benign and malignant states in DDSM mammographic images | Diagnosis and classification of masses in breast tissue | The results of this algorithm are as follows |
| Sensitivity = 93.2% |
| Feature = 78% |
| AUC = 87.7% |
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| 6 | Chougrad et al. [32] | CAD describes a system based on deep CNN that distinguishes malignant and benign breast mass in high-resolution mammographic images; the models used are VGG16, ResNet50, and Inceptionv3 | Classification of masses in breast tissue | With the ResNet50 network in the DDSM dataset, they achieved an accuracy of 27.27% |
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| 7 | Ragab et al. [33] | The region-based method was used to determine the threshold of 76 and determine the most significant area; at the feature extraction stage, DCNN was used; AlexNet network retraining was used to distinguish between the two classes; for better accuracy, the last layer of DCNN was replaced with SVM | Diagnosis of masses in breast tissue | Accuracy, AUC, sensitivity, specificity, accuracy, and F1 score were 80.5%, 88.8%, 88.4%, 84.2%, 86%, and 81.5%, respectively |
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| 8 | Hazarika and Mahanta [34] | The method of extraction of breast border area using threshold-based zoning with morphological operations was proposed; for this purpose, the two-stage image contrast enhancement method was used; in the first phase, a two-step histogram correction technique is used to improve the image at the general level, and in the second phase, a nonlinear filter based on local mean and the local standard deviation for each pixel is applied to the image with the modified histogram | Diagnosis of cancer in breast tissue | The result of the proposed algorithm with 322 images is 98% accuracy |
| 9 | Rajendra et al. [35] | Four texture extraction algorithms were used to extract features from mammographic images, and the SVM classification method was used to classify mammographic images into normal and abnormal categories | Classification of masses in breast tissue | The best results are related to the GLCM feature |
| Accuracy = 92%, sensitivity = 94%, specificity = 93%, accuracy = 95% |
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| 10 | Eltoukhy et al. [36] | The CAD system, based on feature extraction, was applied using the Gauss-Hermitage method, and the features were classified into four different categories: K-NN, random forest, and AdaBoost | Diagnosis of masses in breast tissue | The accuracy of the method used on the two sets of IRMA and MIAS images is 93.27 and 90.56, respectively |
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| 11 | Padmavathy et al. [37] | A practical method was used by NSST + ANFIS to diagnose breast cancer; NSST was used to parse original images in multiple directions and several scales, and ANFIS-compliant clustering was used to classify input images | Diagnosis of masses in breast tissue | The results of the proposed algorithm |
| Accuracy = 98.2%, sensitivity = 90.4%, specificity = 90.6% |
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| 12 | Tahmooresi et al. [38] | A method for the early diagnosis of breast cancer has been proposed. In which it combines different machine learning methods, Support Vector Machine (SVM), ANN, KNN, Decision Tree (DT) | Classification of masses in breast tissue | Accuracy of 99.8% was achieved for the diagnosis of breast cancer |
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| 13 | Amrane et al. [39] | They studied different machine learning methods such as support vector machine, Naive Bayes classifier, and nearest neighbor (KNN) to classify images of breast cancer and claimed that the KNN classification method performed better than vector machine; gain support and Naïve Bayes | Classification of masses in breast tissue | The KNN method has a higher accuracy of 97.57. However, the NB method also has a good accuracy of 96.99% |
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| 14 | Anjaiah et al. [35] | Multi-ROI segmentation is one of the ways these authors have used mammography images; also, images were extracted using statistical criteria to measure the texture characteristics of mammographic images | Classification of masses in breast tissue | This method helps better detect the texture and shape of suspicious mammography images and better diagnose breast cancer |
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| 15 | Vijayarajeswari et al. [40] | They efficiently categorized normal and abnormal classes into mammographic images using Huff transforms; improve results using other features such as mean, variance, and entropy; finally, the SVM cluster was used for classification | Classification of masses in breast tissue | The diagnosis accuracy in standard images is 65%, and in nonnormal images, 71% |
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| 16 | Chowdhary et al. [41] | The goal of this study is to use intuitionistic possibilistic fuzzy c-mean clustering to segment medical photos | Segmentation of breast cancer | For MIAS pictures with varying noise levels of 5%, 7%, and 9% of the presented method, the average segmentation accuracy is 91.25 percent, 87.50 percent, and 85.30 percent |
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| 17 | Chowdhary et al. [42] | Deep convolution neural networks are used to classify breast cancer using computer vision and image processing | Classification of breast cancer | For benign and malignant pictures, the traditional computer vision and image processing paradigm has a classification accuracy of 85 percent and 82 percent, respectively |
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