Treffer: Advances in Support Vector Machines and Deep Learning for Medical Image Analysis: A Comprehensive Review

Every year almost 12 million misdiagnoses used to occur in the U.S. alone, but, deep learning and machine learning is now reducing these errors by detecting tumors from X-rays with 99% sensitivity, rivaling human radiologists. The most recent advancements in deep learning and Support Vector Machines (SVMs) for medical image interpretation are combined in this paper.  We go on implementation tools (TensorFlow, Python), clinical applications (image classification, 3D segmentation, pathology detection), and root approaches.  Important results include MD-GRUs with a 2.3× reduction in 3D segmentation inference time over LSTMs and CNNs with 97.5% accuracy on bacterial picture classification.  Federated learning and explainable AI are proposed as future options after issues like data sparsity and model interpretability are overcome.