Medical imaging involves various clinical areas such are early detection, diagnosis, monitoring, and treatment evaluation for different kinds of disorders. Deep learning in medical imaging technology is a fast-emerging field that detects the occupancy of diseases, interprets and performs diagnosis of the medical images from sources such as X-ray, Computed Tomography, Magnetic Resonance Imaging, Ultrasound, Mammography, Endoscopy, Positron Emission Tomography, digital pathology and more. Deep learning methods provide an efficient way in medical image analysis for automatic diagnosis of diseases and also enable a high level of abstraction and produce better predictions.
Deep learning is well suited to medical big data and can extract useful knowledge from it. It involves the process of automatic detection and quantitative feature analysis of the lesion in medical imaging. The deep learning algorithms for medical image analysis detect any risk and identify anomalies in the medical images. Deep neural networks, Autoencoders, Convolutional neural networks, Deep Boltzmann machines, deep belief networks, General adversarial networks, Recurrent neural networks are some of the deep learning algorithms in medical imaging. Recently, Transfer learning, hybrid model, ensemble model based combined deep learning algorithms have been performed in medical imaging.
The key processes of medical imaging applications are image reconstruction, image segmentation, image registration, image enhancement, computer-aided detection, to name a few. The main application scenarios of deep learning in the medical domain are the classification and segmentation of medical images. The major medical image analysis applications using deep learning are thoracic imaging, neuroimaging, cardiovascular imaging, abdominal imaging, microscopy imaging, skeletal imaging, and gastroenterology imaging. Recent advancements in deep learning in medical imaging are Deep learning in molecular imaging and radiotherapy, 3D deep learning on medical images, Neuroimaging- brain stroke detection, deep learning in Covid-19 medical imaging, and cancer diagnosis.
• Under the booming growth of deep learning in Artificial intelligence (AI), utilizing the advanced deep learning methods for medical image analysis is becoming an active research area in the medical industry.
• Advanced Deep learning methods have arisen as a quickly evolving research and gaining attention in the medical imaging field in evaluating patients.
• The deep learning algorithms have permeated the entire field of medical image analysis which involves classification, detection, segmentation, and registration.
• Due to its stacked layers and hierarchical learning system, deep learning algorithms allows a large amount of data and detect any risk and identify anomalies in the medical images with high accuracy, efficiency, stability, and scalability.
• Recent deep learning applications in medical image analysis involve various computer vision-related tasks such as classification, detection, segmentation, and registration. Among them, classification, detection, and segmentation are the most widely used tasks.
• Despite the numerous advantages of deep learning-based medical image analysis, complexities and challenges exist, such as small-scale medical datasets and class imbalance are the main bottleneck in the medical field.