Prediction of Osteosarcoma Bone Cancer Using Convolutional Neural Networks and Multi-Feature Integration Methods

Osteosarcoma is the most aggressive type of bone cancer often associated with high morbidity, which makes it challenging to diagnose and prognosticate. In this paper, we present a completely new framework for the prediction of osteosarcoma bone cancer using convolutional neural networks CNNs in conjunction with more modern agile feature fusion methods. The proposed model is envisioned to enhance the accuracy of diagnosis through the correct modeling of complex patterns within medical imaging datasets. The model reinforces its performance on earlier markers of osteosarcoma by incorporating multiple feature extraction techniques which enables results that can facilitate prompt mitigation. We conduct our assessment on a custom-built dataset of radiographic images to establish its viability against traditional handover methods. The findings pointed to the fact that such modalities have great potential in augmenting the arsenal of diagnostic approaches to the investigation of osteosarcoma and might improve the clinical decision-making process as well as the clinical outcome of patients.