Deep CNN Architectures for Predicting Malignant Bone Tumors from MRI Scans

A malignant bone tumor is a rare but aggressive form of cancer that requires surgical removal before metastasizing and compromising other organs. Pre-operative localization of these tumors is key for surgery. However, current imaging methods applied in a standard MRI protocol might only sometimes be able to pinpoint the exact nature of certain types. To solve this problem, deep convolutional neural networks (CNNs) were introduced as a possible way to predict benign and malignant bone tumors detected on MRI. Deep CNNs are powerful machine learning algorithms that can learn complex features from data and the classifier without explicit feature engineering. They are especially good for processing large, highly apparent medical images, as found in MRI scans. Though many state-of-the-art deep CNN architectures have been suggested for automatically detecting malignant bone tumors, they performed with different success rates. This architecture is employed by structures that include various convolutions, pooling, and nonlinear activations, and it helps the models learn spatially or temporally associated features in MRI images. These deep CNN architectures were performed with high accuracy and sensitivity in predicting malignant bone tumors from MRI scans. It could enhance the early detection and treatment of these deadly cancers. Future work in strengthening and fine-tuning these deep CNN architectures could improve the accuracy and efficacy of predicting malignant bone tumors from MRI scans, which may translate to better clinical outcomes.