Abstract:
Accurate diagnosis and classification of bone nodules are required for effective treatment planning and patient care. In this work, we introduce an innovative approach using artificial intelligence (AI) for segmentation and classification of nodules in bone tumor cases. It aims to develop and validate advanced artificial intelligence models for improving accuracy and efficiency of bone tumor detection, diagnosis at the medical imaging level. The proposed method consists of two steps: segmentation and classification. In the segmentation stage, tumor regions in magnetic resonance imaging (MRI) are detected & traced via Convolutional Neural Networks (CNNs), a DL method for image analysis. On the other hand, it is commented that to identify tumor nodules from surrounding tissues, together all MRI image data are used to train the segmentation model. At the classification step, based on their Mets and HIS features, tumor nodules are split into segments using combination of methods from AI tools: transfer learning /ensembling. Ensure valid results across various types of tumors by improving and testing the model with performance metrics (accuracy, sensitivity, specificity). Results from the study highlight that the artificially intelligent approach boosts diagnostic accuracy and reliability for bone tumors, far beyond the level of conventional methods. The model is practical for radiologists and oncologists using the given designed model where it is efficient in segmentation and classification tasks.
