Deep Learning: A Breakthrough in Medical Imaging

Abstract

: Deep learning has attracted great attention in the medical imaging community as a promising solution for automated, fast and accurate medical image analysis, which is mandatory for quality healthcare. Convolutional neural networks and its variants have become the most preferred and widely used deep learning models in medical image analysis. In this paper, concise overviews of the modern deep learning models applied in medical image analysis are provided and the key tasks performed by deep learning models, i.e. classification, segmentation, retrieval, detection, and registration are reviewed in detail. Some recent researches have shown that deep learning models can outperform medical experts in certain tasks. With the significant breakthroughs made by deep learning methods, it is expected that patients will soon be able to safely and conveniently interact with AI-based medical systems and such intelligent systems will actually improve patient healthcare. There are various complexities and challenges involved in deep learning-based medical image analysis, such as limited datasets. But researchers are actively working in this area to mitigate these challenges and further improve health care with AI.

Modified Convolutional Neural Networks Architecture for Hyperspectral Image Classification (Extra‐Convolutional Neural Networks)

“…Classification of satellite images is a very interesting technology for remote sensing, which can help in several areas such as cartography, security, ground control, and natural disasters [1][2][3]. Deep learning technologies are the ideal solutions for better classification of different types of images (satellite [4,5], drones, medical [6], facial [7], etc.). The classification of hyperspectral images by convolutional neural networks (CNN) has attracted the attention of researchers, by the perfect results obtained in recent years [8][9][10].…”

New convolutional neural network model for screening and diagnosis of mammograms

“…Convolutional neural network (CNN) is an important branch of deep learning, which is widely used in the field of natural image analysis. Inspired by this, some researchers try to apply CNN to the field of medical image analysis [ 15 – 17 ] to help radiologists make correct judgments. In order to verify the effectiveness of CNN model in clinical diagnosis, Dontchos et al [ 18 ] introduced external validation to evaluate the performance of their proposed model.…”

Critical insights into modern hyperspectral image applications through deep learning

“…Nowadays, the duo of deep learning and hyperspectral imaging is gaining a significant boom in the medical imaging domain due to its quick, valid, automated, and promising results (Ahmad et al, 2020). This section focuses on the medical applications through deep learning and hyperspectral imaging.…”