The six different models were trained by modification of these models on 5 datasets that were taken from the same Breakhis database, that is, we divided by the mag- nification factor and took all the data. Using an 80% training set and 20% test set, all models fine-tuning all layers and classified malignant and benign cancer types.
Several conventional performance metrics were extracted from confusion matrices, including true positive (TP), false positive (FP), true negative (TN), false negative (FN), accuracy, precision, recall, and F-1 score.
π΄πππ’ππππ¦ = π π +π π
π π +π π+πΉ π +πΉ π (1)
π πππππ πππ== π π
π π +πΉ π (2)
π πππππ= π π
(π π +πΉ π) (3)
πΉ β1π ππππ= 2* π πππππ ππππ πππππ
(π πππππ πππ+π πππππ) (4)
Models Accuracy Precision Recall F-1 Score Efficientnetv2-b0 0,945 0,95 0,945 0,945
Mobilenet-v2 0,9349 0,935 0,935 0,9349
Resnetv2-50 0,94075 0,94 0,94 0,94075
Inception-v3 0,8395 0,855 0,84 0,8395 Inception-Resnet-v2 0,8468 0,845 0,845 0,8468
VGG16 0,9378 0,938 0,938 0,9378
Table 4.1: The results of models on overall BreaKHis data
Figure 4-2: The graphical representation of model results on Table 4.1
Figure 4-3: Confusion Matrix of Efficientnetv2-b0
According to the results of Table 4.1, the highest results were shown by the mod- els: Efficientnetv2, Mobilenet-v2, Resnetv2-50, and VGG16. However, the best result was shown by Efficientnetv2, with a confident 94.5% accuracy. Overall, Efficientnetv2 has worked very well and represented top results in comparison to other CNN archi- tectures, but for data with a magnification factor of 400 had worked as expected Table 4.2. In Figure 4.4, we can see the model execution and variation of train and validation accuracy. For the data which 400x magnification through a microscope, better results were shown by the pre-trained CNN architecture Inception-Resnet-v2 with 88,5% accuracy. Based on the experiments done, it can be assumed that the Efficientnetv2-b0 is a suitable model for the analysis of histopathological images. As the results show, the magnification factor affects the evaluation of the model because the parameters set in the models and the image parameters differ because of this, the evaluation of the model decreases. For example, with a magnification coefficient of 400, almost all models show a relatively low accuracy, since when histopatholog- ical images are magnified by 400, a lot of picture parameters appear that do not correspond to the expectation of the models.
Figure 4-4: Model Execution and Plot of Training Accuracy by Validation Accuracy The best model, as discussed above, Efficientnetv2-b0, was trained by modification where we added 3 layers and updated all weights of these pre-trained deep CNN architecture for 12 epochs with cross-validation and batch size was 64, validation
accuracy was generated between 92-99%, but in test data, it showed 94.5% on other shots it varies from 94% to 97% that shows a high result. In Figure 4.3, we can observe a confusion matrix that summarizes the prediction results of the improved Efficientnetv2-b0 model.
Papers Methods 40x 100x 200x 400x
Fabio et al.[38] PFTAS + SVM 0.816 0.799 0.851 0.823
Han et al.[39] AlexNet + Aug 0.856 0.835 0.831 0.808
Farjana Parvin et al.[24] pre-trained CNN 0.89 0.92 0.94 0.9 Our proposed study pre-train CNN + Aug + Opt Par 0.955 0.93 0.98 0.885 Table 4.3: The comparison results of previous studies by our proposed study
Table 4.3 displays the accuracy results of previous papers related to the classifi- cation of breast cancer type on histopathological images. All this research done on BreaKHis data set and our proposal work represents higher accuracy results than state-of-the-art other models. However, Han et al.[39] achieved an average 93.8%
accuracy by the CSDCNN model, which means that the results are less than our pro- posed study results. In addition, we can inform that our model is such an excellent methodology for the detection of breast cancer.
Models Magnification Factor Accuracy Precision Recall F-1 Score
Efficientnetv2-b0 40x 0,955 0,955 0,95 0,95
100x 0,93 0,93 0,93 0,93
200x 0,98 0,98 0,98 0,98
400x 0,81 0,81 0,845 0,81
Mobilenet-v2 40x 0,645 0,645 0,645 0,645
100x 0,755 0,755 0,805 0,755
200x 0,84 0,83999 0,85 0,84
400x 0,87 0,87 0,86 0,86
Resnet-50 40x 0,87 0,87 0,86 0,86
100x 0,805 0,805 0,85 0,805
200x 0,84 0,84 0,845 0,84
400x 0,74 0,74 0,75 0,74
Inception-v3 40x 0,71 0,71 0,715 0,72
100x 0,67 0,67 0,785 0,67
200x 0,715 0,715 0,8 0,715
400x 0,595 0,595 0,745 0,595
Inception-Resnet-v2 40x 0,925 0,925 0,925 0,925
100x 0,67 0,67 0,785 0,67
200x 0,945 0,945 0,95 0,945
400x 0,885 0,885 0,89 0,885
VGG16 40x 0,925 0,925 0,95 0,9
100x 0,905 0,905 0,92 0,89
200x 0,93 0,93 0,97 0,925
400x 0,825 0,825 0,845 0,82
Table 4.2: The results of models according to magnification factors
Chapter 5 Conclusion
Breast cancer is the most common type of cancer among women, and it is the most common single cause of death among all women aged 35 to 54. Nowadays, with the help of advanced technologies and the development of machine learning, tumor type detection is becoming accessible and fast. This paper compared and modified several deep trained models by changing the parameters and adding extra layers and realized that it is not always possible to get the desired results through trained CNN architectures. It is essential to be able to prepare data and build favorable data for these pre-trained architectures. After all the experiments, we can make an assumption that the quality of the data decides 60-70% of the success of the model. Our initial task was to compare and modify the models, select convenient parameters for these models and develop these models by using optimization methods, fine-tuning, apply suitable data augmentations methods to these models. Based on the BreaKHis data, a total of 30 models were trained, of which, on average, we achieved 90% and above.
The best result showed EfficientNet 94.5% accuracy on total data, and on average, it showed 91.87% accuracy on test data for 4 magnifying factors and in comparison with other state of the art studies represented the higher result. For future work, we will implement a data fusion methodology that will compare the results of the best models to help improve accuracy and add a Generative adversarial network technique to enrich the complex images to improve the quality of the model. In conclusion, as we discussed earlier, methods for detecting malignant and benign breast cancer gland
is a complex procedure, and we expect our methods to work as intended, and these long processes can help to identify the type of tumor, helping to save lives.
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