Deep Learning Applications in Medical Image Analysis: Advancements, Challenges, and Future Directions
It addresses the problem of enhancing diagnostic processes in healthcare for medical professionals, but it is incremental as it synthesizes existing advancements without introducing new methods.
This paper reviews how deep learning, particularly CNNs, has improved the accuracy and efficiency of medical image analysis for tasks like detection and classification, though it does not provide specific numerical results.
Medical image analysis has emerged as an essential element of contemporary healthcare, facilitating physicians in achieving expedited and precise diagnosis. Recent breakthroughs in deep learning, a subset of artificial intelligence, have markedly revolutionized the analysis of medical pictures, improving the accuracy and efficiency of clinical procedures. Deep learning algorithms, especially convolutional neural networks (CNNs), have demonstrated remarkable proficiency in autonomously learning features from multidimensional medical pictures, including MRI, CT, and X-ray scans, without the necessity for manual feature extraction. These models have been utilized across multiple medical disciplines, including pathology, radiology, ophthalmology, and cardiology, where they aid in illness detection, classification, and segmentation tasks......