# A Deep-Learning Approach for Diagnosis of Metastatic Breast Cancer in Bones from Whole-Body Scans
## 5 Discussion  
Through the trials and experiments performed, it emerges that the accuracy of the selected CNN architecture is higher in the case of RGB images. Also, the loss (validation and testing) is quite low, and the confusion matrix is better in terms of accuracy than the corresponding one in the case of grayscale images, showing the superiority of RGB images use for the classification task. The CNN in RGB process spent 4 min approximately for training each fold dataset and less than 30 s for prediction. The best performing CNN algorithm was then compared with some state-of-the art CNNs that are commonly used for image classification problems. To directly compare the classification performance of our model with other popular CNN architectures we used, besides the accuracy, the evaluation metrics of precision, recall, F1 score, sensitivity and specificity indicators, which are listed in the tables above. We highlight that our proposed algorithm performed better when compared to popular CNN algorithms for the specific problem. This shows that the improvements made to network architecture and the configuration of hyperparameter were rather effective. This study validates the premise that CNNs are algorithms that can offer high accuracy in medical image classification-based problems. This can have a direct application on medical imaging where the automatic identification of diseases is crucial for the patients. The main outcomes of this study can be summarized as follows:  
* The proposed CNN method exhibits outstanding performance when RGB analysis is performed, for the examined images of this case study. This can emanate from the fact that the results produced from the application of the CNN architecture are based on distinct features that appear specifically on the bone metastasis presence scans, compared to the healthy scans (no malignant spots). * The proposed CNN architecture is superior compared to three out of four benchmarks and well-known CNN architectures (ResNet50, VGG16, MobileNet, and DenseNet) which have been previously and efficiently used in medical image-processing problems. As observed from the aforementioned tables and figures, the results deriving from the application of the CNN method,  
Figure 8: Average run time for the compared best CNN architectures. are better in terms of classification accuracy, prediction, sensitivity, specificity and f1 score, than those coming from popular and well-known CNN approaches found in the relevant literature. Even if CNN has an inferior performance compared to DenseNet, the accuracy and loss values of the proposed CNN show a quite low variability whereas the corresponding DenseNet values present substantial variations among predicted outcomes (see Figure A3). * The proposed bone scan deep learning performs efficiently despite the fact that it was trained on a small number of images. * Overall, the deployed process seemingly improves the diagnostic effect of the deep-learning method, making it more efficient compared to other benchmark CNN architectures for image analysis. For the purpose of medical image analysis and classification, the CNN approach can be used effectively in the classification of whole-body images in nuclear medicine, outweighing the popular CNN architectures for medical image analysis. To sum up, this is the first research study concerning the application of CNNs efficiently in bone metastasis diagnosis from breast cancer patients in bone scintigraphy. Following the proposed methodology, a robust CNN algorithm is created for bone metastasis diagnosis using whole-body scans from breast cancer patients. This algorithm can be implemented in any system without specific requirements, providing the nuclear medicine physician with an efficient and robust decision support tool to automatically identify whether a patient is suffering from breast cancer or not, by looking at whole-body scintigraphy images.