Deep Learning Models for Medical Image Analysis: Advances in STEM Methodologies
Keywords:
Deep learning, medical image analysis, STEM methodologies, convolutional neural networks, radiology, tumor segmentation, transfer learning, explainable AI, federated learning, biomedical engineering.Abstract
Abstract
Recent advances in deep learning have revolutionized medical image analysis, offering transformative improvements in diagnostic accuracy, disease detection, and treatment planning. This progress is largely fueled by enhanced STEM methodologies that integrate computational power, mathematical modeling, and biomedical engineering. Deep learning architectures, particularly convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, have demonstrated superior performance in analyzing complex radiological, histopathological, and retinal images. These models excel in tasks such as tumor segmentation, organ delineation, and anomaly detection, outperforming traditional machine learning techniques and even expert radiologists in some cases.
