# A Deep-Learning Approach for Diagnosis of Metastatic Breast Cancer in Bones from Whole-Body Scans
## 1 Introduction
### Motivation and Aim of This Research Study  
Although bone scintigraphy is extremely important for diagnosis of metastatic cancer in bones, in both men and women, there is currently no research paper regarding the classification of breast cancer metastasis in bones from whole-body images (bone scintigraphy) that uses efficient and robust CNNs. Nowadays, the main challenge in bone scintigraphy, which is considered one of the most sensitive methods for imaging in nuclear medicine, regards building an algorithm that automatically identifies whether a patient is suffering from bone metastasis or not, only by looking at the whole-body scans. The algorithm has to be extremely accurate since people's lives could be at stake. Machine-learning approaches, with a focus on deep learning algorithms, have particularly shown a promising applicability in medical image analysis in the area of nuclear medicine. However, classification accuracy of bone scintigraphy, analyzed by machine-learning approaches, involving CNN systems, has not been well established yet. The current work introduces an efficient CNN method for the diagnosis of bone metastasis disease, by identifying whether a patient is suffering from bone metastasis or not, based on whole-body images. After a thorough CNN exploration, taking into consideration both network architecture and hyperparameter configuration, the researchers propose a robust CNN to classify images in two classes, benign and malignant. A retrospective study involving 408 women patients suffering from breast cancer, was conducted to examine the accuracy of bone scintigraphy analyzed by CNNs. For validation purposes of the proposed classification methodology, a meticulous comparison analysis was performed between the proposed CNN method and a number of well-known and popular CNN architectures. In this work, popular and advanced CNN structures such as ResNet50, VGG16, and DenseNet that were previously proposed for medical image classification [22; 23; 24; 30], are applied in the dataset, after their necessary parameterization and configuration. The experimental results reveal that the proposed method achieves superior performance as far as bone scintigraphy is concerned, outperforming other well-known methods of CNNs. The novel contribution of this research work lies in the creation of a robust CNN model (being the most efficient deep learning method in medical image analysis) for bone metastasis diagnosis from breast cancer patients using whole-body scans. Following the literature review and the state-of-the-art deep learning methodology (as presented in Sections 1.1 and 1.2), it is obvious that there is no previous work on metastatic breast cancer identification in bones from whole-body scans using deep learning and CNNs. The innovations and contributions of this paper are summarized as follows:  
* The creation and demonstration of a customized, robust CNN-based classification tool for identification of metastatic breast cancer in bones from whole-body scans. * The meticulous exploration of CNN hyper-parameter selection to define the best architecture and select the RGB mode selection that could lead to enhanced classification performance. * The comparative experimental analysis performed for utilizing popular image classification CNN architectures, like ResNet50, VGG16, GoogleNET, etc. The proposed methodology apparently improves the diagnostic effect of the deep-learning method. Moreover, after a thorough assessment of the results produced, the proposed approach is proven to be effective for classification of whole-body images in nuclear medicine. This paper is structured in the following fashion: Section 2 presents the material and methods related to this research study. The proposed network based on CNN for bone metastasis diagnosis in breast cancer using whole body images is described in Section 3. The exploration analysis of CNNs in both RGB and grayscale modes is illustrated in Section 4, considering different parameters and configurations, thus providing the best CNN model for this case study.