Journal ArticleDOI
A Review of Deep Learning on Medical Image Analysis
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TLDR
In this article, a comprehensive review of transfer learning in medical image analysis is presented, including the structure of CNN, background knowledge, different types of strategies performing transfer learning, different sub-fields of analysis, and discussion on the future prospect for transfer learning.Abstract:
Compared with common deep learning methods (e.g., convolutional neural networks), transfer learning is characterized by simplicity, efficiency and its low training cost, breaking the curse of small datasets. Medical image analysis plays an indispensable role in both scientific research and clinical diagnosis. Common medical image acquisition methods include Computer Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound (US), X-Ray, etc. Although these medical imaging methods can be applied for non-invasive qualitative and quantitative analysis of patients—compared with image datasets in other computer vision fields such like faces—medical images, especially its labeling, is still scarce and insufficient. Therefore, more and more researchers adopted transfer learning for medical image processing. In this study, after reviewing one hundred representative papers from IEEE, Elsevier, Google Scholar, Web of Science and various sources published from 2000 to 2020, a comprehensive review is presented, including (i) structure of CNN, (ii) background knowledge of transfer learning, (iii) different types of strategies performing transfer learning, (iv) application of transfer learning in various sub-fields of medical image analysis, and (v) discussion on the future prospect of transfer learning in the field of medical image analysis. Through this review paper, beginners could receive an overall and systematic knowledge of transfer learning application in medical image analysis. And policymaker of related realm will benefit from the summary of the trend of transfer learning in medical imaging field and may be encouraged to make policy positive to the future development of transfer learning in the field of medical image analysis.read more
Citations
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Journal ArticleDOI
Demystifying Supervised Learning in Healthcare 4.0: A New Reality of Transforming Diagnostic Medicine
TL;DR: The effectiveness and potential for innovation of disease diagnosis, personalized medicine, clinical trials, non-invasive image analysis, drug discovery, patient care services, remote patient monitoring, hospital data, and nanotechnology in various learning-based automation in healthcare along with the requirement for explainable artificial intelligence in healthcare are evaluated.
Journal ArticleDOI
Hybrid Intelligence-Driven Medical Image Recognition for Remote Patient Diagnosis in Internet of Medical Things
TL;DR: In this article , a hybrid intelligence-driven medical image recognition framework was proposed for remote patient diagnosis in IoMT, which combines deep learning with the conventional machine learning (CML)-based methods.
Journal ArticleDOI
A deep-learning based multimodal system for Covid-19 diagnosis using breathing sounds and chest X-ray images.
Unais Sait,Gokul Lal K,Sanjana Shivakumar,Tarun Kumar,Rahul Bhaumik,Sunny Prakash Prajapati,Kriti Bhalla,Anaghaa Chakrapani +7 more
TL;DR: In this paper, a transfer learning approach using existing deep-learning Convolutional Neural Network (CNN) based on Inception-v3 is combined with Multi-Layered Perceptron (MLP) to develop the CovScanNet model for reducing false negatives.
Journal ArticleDOI
The Role of Generative Adversarial Network in Medical Image Analysis: An In-depth Survey
Manal Alamir,Manal AlGhamdi +1 more
TL;DR: The objective of this review is to provide a comprehensive overview of the GAN, simplify the GAn’s basics, and present the most successful applications in different scenarios.
A transfer learning method with deep convolutional neural network for diffuse lung disease classification
TL;DR: A deep convolutional neural network (DCNN) is introduced as feature extraction method in a computer aided diagnosis (CAD) system in order to support diagnosis of diffuse lung diseases (DLD) on high-resolution computed tomography (HRCT) images.
References
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Proceedings ArticleDOI
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TL;DR: The objective here is to understand better why standard gradient descent from random initialization is doing so poorly with deep neural networks, to better understand these recent relative successes and help design better algorithms in the future.