Bone age assessment based on deep learning architecture

Abstract

<span lang="EN-US">The fast advancement of technology has prompted the creation of automated systems in a variety of sectors, including medicine. One application is an automated bone age evaluation from left-hand X-ray pictures, which assists radiologists and pediatricians in making decisions about the growth status of youngsters. However, one of the most difficult aspects of establishing an automated system is selecting the best approach for producing effective and dependable predictions, especially when working with large amounts of data. As part of this work, we investigate the use of the convolutional neural networks (CNNs) model to classify the age of the bone. The work’s dataset is based on the radiological society of North America (RSNA) dataset. To address this issue, we developed and tested deep learning architecture for autonomous bone assessment, we design a new deep convolution network (DCNN) model. The assessment measures that use in this work are accuracy, recall, precision, and F-score. The proposed model achieves 97% test accuracy for bone age classification.</span>

Automated Identification and Classification of Brain Tumors Using Hybrid Machine Learning Models and MRI Imaging

“…This necessitates the development of advanced techniques anchored in deep learning and machine learning. CAD systems, encompassing deep learning and machine learning-based diagnostic systems, are founded on principles of magnetic resonance imaging (MRI), a cornerstone modality in medical imaging [8,9]. MRI is routinely employed to visualize aberrant brain tissues [10].…”