Convolutional neural networks for medical image analysis: State-of-the-art, comparisons, improvement and perspectives

Yu, Hang; Yang, Laurence T.; Zhang, Qingchen; Armstrong, David; Deen, M. Jamal

doi:10.1016/j.neucom.2020.04.157

Cited by 229 publications

(87 citation statements)

References 109 publications

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“…In segmentation (semantic segmentation), the target object edges are surrounded by outlines, which also label them; moreover, fitting a single image (which could be 2D or 3D) onto another refers to registration. One of the most important and wide-ranging DL applications are in healthcare [225][226][227][228][229][230]. This area of research is critical due to its relation to human lives.…”

Section: Applications Of Deep Learningmentioning

confidence: 99%

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

et al. 2021

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In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion.

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Section: Applications Of Deep Learningmentioning

confidence: 99%

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

et al. 2021

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“…Related scholars have successfully applied neural networks to short-term stock index forecasts, and related researchers have continuously proposed various types of neural network structures and applied them in financial forecasts [ 17 – 19 ]. Researchers used an adaptive BP neural network to predict the changing trends of two important stock indexes, S&P500 and NIKKEI225, and achieved good results [ 20 , 21 ]. Researchers combined knowledge discovery (MMDR) with neural networks and achieved prediction results superior to traditional methods [ 22 – 24 ].…”

Section: Background and Related Workmentioning

confidence: 99%

[Retracted] Construction and Simulation of Financial Audit Model Based on Convolutional Neural Network

Zhang

2021

Computational Intelligence and Neuroscience

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Big data has brought a new round of information revolution. Faced with the goal of full coverage of audit and supervision, making full use of big data is the main method to promote the realization of the goal of full coverage of audit and supervision. Data analysis and utilization is an indispensable task of auditing. Actively exploring multidimensional and intelligent data analysis methods and developing big data audit cases are the new development direction of auditing. The convolutional neural network’s excellent ability to extract data features well meets the relevant requirements of financial auditing. However, in practical applications, convolutional neural networks often encounter various problems such as disappearance of gradients and difficulty in convergence, which reduces its expected performance in financial audit applications. In order to make the performance of the financial audit model based on convolutional neural network more excellent, after summarizing the characteristics of genetic algorithm, this article applies genetic algorithm to the optimization of the convolutional neural network model. We applied genetic algorithm to optimize the initial weights of the convolutional neural network. The error sensitivity and learning rate changes of different hidden layers are discussed, the influence of different learning rates on the convergence speed of convolutional neural networks is analyzed, and the recognition performance of other algorithms on financial audit data sets is simulated and compared. We conducted experiments on the network structure and parameter optimization on the financial audit database. The results show that the recognition error rate of the convolutional neural network model with improved learning rate algorithm in the financial audit data set is lower than that of the multilayer feedforward network, so it has better performance.

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“…Among deep learning algorithms, autoencoders have been increasingly used in medical applications in recent years 26 , 27 . Although CNN is one of the most known and used deep learning algorithms, it is often preferred especially in the analysis of image data and provides successful results 28 , 29 . Therefore, to classify 1D SERS signals of MRSA and MSSA stacked autoencoder (SAE) based deep neural network (DNN) was preferred in this study.…”

Section: Introductionmentioning

confidence: 99%

Drug-resistant Staphylococcus aureus bacteria detection by combining surface-enhanced Raman spectroscopy (SERS) and deep learning techniques

Ciloglu

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Saridag

et al. 2021

Sci Rep

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Over the past year, the world's attention has focused on combating COVID-19 disease, but the other threat waiting at the door—antimicrobial resistance should not be forgotten. Although making the diagnosis rapidly and accurately is crucial in preventing antibiotic resistance development, bacterial identification techniques include some challenging processes. To address this challenge, we proposed a deep neural network (DNN) that can discriminate antibiotic-resistant bacteria using surface-enhanced Raman spectroscopy (SERS). Stacked autoencoder (SAE)-based DNN was used for the rapid identification of methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-sensitive S. aureus (MSSA) bacteria using a label-free SERS technique. The performance of the DNN was compared with traditional classifiers. Since the SERS technique provides high signal-to-noise ratio (SNR) data, some subtle differences were found between MRSA and MSSA in relative band intensities. SAE-based DNN can learn features from raw data and classify them with an accuracy of 97.66%. Moreover, the model discriminates bacteria with an area under curve (AUC) of 0.99. Compared to traditional classifiers, SAE-based DNN was found superior in accuracy and AUC values. The obtained results are also supported by statistical analysis. These results demonstrate that deep learning has great potential to characterize and detect antibiotic-resistant bacteria by using SERS spectral data.

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Convolutional neural networks for medical image analysis: State-of-the-art, comparisons, improvement and perspectives

Cited by 229 publications

References 109 publications

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

[Retracted] Construction and Simulation of Financial Audit Model Based on Convolutional Neural Network

Drug-resistant Staphylococcus aureus bacteria detection by combining surface-enhanced Raman spectroscopy (SERS) and deep learning techniques

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