A Review Paper about Deep Learning for Medical Image Analysis (original) (raw)

Deep Learning in Medical Image Analysis

Annual review of biomedical engineering, 2017

This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. At the core of these advances is the ability to exploit hierarchical feature representations learned solely from data, instead of features designed by hand according to domain-specific knowledge. Deep learning is rapidly becoming the state of the art, leading to enhanced performance in various medical applications. We introduce the fundamentals of deep learning methods and review their successes in image registration, detection of anatomical and cellular structures, tissue segmentation, computer-aided disease diagnosis and prognosis, and so on. We conclude by discussing research issues and suggesting future directions for further improvement.

On the Use of Deep Learning Methods on Medical Images

2019

Deep Learning algorithms have recently been reported to be successful in the analysis of images and voice. These algorithms, specifically Convolutional Neural Network (CNN), have also proven themselves to be highly promising on images produced by medical imaging technologies, as well. By use of deep learning algorithms, researchers have accomplished several tasks in this field including image classification, object and lesion detection and segmentation of different tissues in a medical image. Researchers mostly focused on medical images of neurons, retina, lungs, digital pathology, breast, heart, abdomen and skeleton system to take advantage of the Deep Learning approach. This study reviews literature studies of recent years that utilized Deep Learning algorithms on medical images in order to present a general picture of the relevant literature.

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.

A Comprehensive Review on the Significance and Impact of Deep Learning in Medical Image Analysis

2021 International Conference on Technological Advancements and Innovations (ICTAI)

Healthcare sectors have evolved over the years to remain as one of the most demanding and important aspect of human lives requiring immediate services and attention in difficult times but the entire process is quite tedious and time-consuming when performed by the medical experts. However, with the advent of AI based machine learning or deep learning techniques, the medical image analysis task became quite smoother, faster and efficient delivering more optimized performances. This manuscript briefs us about the various deep learning techniques and methodologies being applied till date in the domain of medical image processing besides laying emphasis on the overview of recent advances and overall contributions being made in this field along with its associated challenges. It also throws light on the future perspective to overcome those challenges specifically using better and innovative approaches.

Recent advances and clinical applications of deep learning in medical image analysis

Medical Image Analysis

Deep learning has become the mainstream technology in computer vision, and it has received extensive research interest in developing new medical image processing algorithms to support disease detection and diagnosis. As compared to conventional machine learning technologies, the major advantage of deep learning is that models can automatically identify and recognize representative features through the hierarchal model architecture, while avoiding the laborious development of hand-crafted features. In this paper, we reviewed and summarized more than 200 recently published papers to provide a comprehensive overview of applying deep learning methods in various medical image analysis tasks. Especially, we emphasize the latest progress and contributions of state-of-the-art unsupervised and semi-supervised deep learning in medical images, which are summarized based on different application scenarios, including lesion classification, segmentation, detection, and image registration. Additionally, we also discussed the major technical challenges and suggested the possible solutions in future research efforts.

Medical Images Analysis Using Deep Learning Technique

International Journal of Wireless Communications and Network Technologies, 2024

Clinical picture classification, pattern recognition, and quantification have seen significant advancements with the help of artificial intelligence, particularly through deep learning techniques. Deep learning has rapidly emerged as the most rapidly evolving field within AI, and its applications have been successfully demonstrated across various domains, including medicine. This review briefly examines recent applied research in several medical fields, such as neurology, brain imaging, retinal analysis, pneumonics, computerized pathology, breast imaging, cardiovascular studies, musculoskeletal imaging, and gastrointestinal imaging. Deep learning networks prove to be highly effective when dealing with large-scale medical datasets, enabling information discovery, knowledge dissemination, and knowledge-based prediction. This research aims to present both foundational knowledge and state-of-the-art deep learning techniques to facilitate the interpretation and analysis of medical images. The primary objectives of this work are to explore advancements in medical image processing research and implement the identified and addressed key criteria in practical applications.

Deep Learning Applications in Medical Image Analysis

IEEE Access

The tremendous success of machine learning algorithms at image recognition tasks in recent years intersects with a time of dramatically increased use of electronic medical records and diagnostic imaging. This review introduces the machine learning algorithms as applied to medical image analysis, focusing on convolutional neural networks, and emphasizing clinical aspects of the field. The advantage of machine learning in an era of medical big data is that significant hierarchal relationships within the data can be discovered algorithmically without laborious hand-crafting of features. We cover key research areas and applications of medical image classification, localization, detection, segmentation, and registration. We conclude by discussing research obstacles, emerging trends, and possible future directions.

Medical image analysis based on deep learning approach

Multimedia Tools and Applications

Medical imaging plays a significant role in different clinical applications such as medical procedures used for early detection, monitoring, diagnosis, and treatment evaluation of various medical conditions. Basicsof the principles and implementations of artificial neural networks and deep learning are essential for understanding medical image analysis in computer vision. Deep Learning Approach (DLA) in medical image analysis emerges as a fast-growing research field. DLA has been widely used in medical imaging to detect the presence or absence of the disease. This paper presents the development of artificial neural networks, comprehensive analysis of DLA, which delivers promising medical imaging applications. Most of the DLA implementations concentrate on the X-ray images, computerized tomography, mammography images, and digital histopathology images. It provides a systematic review of the articles for classification, detection, and segmentation of medical images based on DLA. This review guides the researchers to think of appropriate changes in medical image analysis based on DLA.

Review Research of Medical Image Analysis Using Deep Learning

UHD Journal of Science and Technology

In modern globe, medical image analysis significantly participates in diagnosis process. In general, it involves five processes, such as medical image classification, medical image detection, medical image segmentation, medical image registration, and medical image localization. Medical imaging uses in diagnosis process for most of the human body organs, such as brain tumor, chest, breast, colonoscopy, retinal, and many other cases relate to medical image analysis using various modalities. Multi-modality images include magnetic resonance imaging, single photon emission computed tomography (CT), positron emission tomography, optical coherence tomography, confocal laser endoscopy, magnetic resonance spectroscopy, CT, X-ray, wireless capsule endoscopy, breast cancer, papanicolaou smear, hyper spectral image, and ultrasound use to diagnose different body organs and cases. Medical image analysis is appropriate environment to interact with automate intelligent system technologies. Among t...

Overview of machine learning: part 2: deep learning for medical image analysis

Neuroimaging Clinics of North America, 2020

KEY POINTS - Radiological imaging data for H&N contains a wealth of information suitable for feature extraction using deep learning methods to characterize various pathologies. - Convolutional neural networks (CNN) have recently become highly effective in multiple medical imaging tasks including anatomical classification, segmentation and registration, as well as disease progress prediction, and image reconstruction. - Various experimental and ethical considerations still need to be addressed to ensure successful deployment of deep learning models in clinical settings.