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Lo, Shih-Chung Benedict; Lin, Jyh-Shyan; Freedman, Matthew T.; Mun, Seong Ki

doi:10.1023/a:1007941315275

Cited by 17 publications

(1 citation statement)

References 25 publications

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“…As mentioned earlier in the previous section, single neural networks have been widely used not only in the engineering filed but also in other applications like remote sensing, [60] transportation, power system, [61] medicine, [62,63] telecommunication, banking and also application in robotics and vision techniques. [64] The growing interest in applying single neural networks are due to the computing systems that grow rapidly and enable the behavior of the complex system to be modeled and predicted accurately.…”

Section: Multiple Neural Network In Modeling and Control Applicationmentioning

confidence: 99%

Multiple neural networks modeling techniques in process control: a review

Ahmad

Noor

Zhang

2009

Asia-Pacific J Chem Eng

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This paper reviews new techniques to improve neural network model robustness for nonlinear process modeling and control. The focus is on multiple neural networks. Single neural networks have been dominating the neural network 'world'. Despite many advantages that have been mentioned in the literature, some problems that can deteriorate neural network performance such as lack of generalization have been bothering researchers. Driven by this, neural network 'world' evolves and converges toward better representations of the modeled functions that can lead to better generalization and manages to sweep away all the glitches that have shadowed neural network applications. This evolution has lead to a new approach in applying neural networks that is called as multiple neural networks. Just recently, multiple neural networks have been broadly used in numerous applications since their performance is literally better than that of those using single neural networks in representing nonlinear systems. 

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Section: Multiple Neural Network In Modeling and Control Applicationmentioning

confidence: 99%

Multiple neural networks modeling techniques in process control: a review

Ahmad

Noor

Zhang

2009

Asia-Pacific J Chem Eng

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A systematic survey of deep learning in breast cancer

Zhou

Wang

et al. 2021

Int J Intell Syst

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In recent years, we witnessed a speeding development of deep learning in computer vision fields like categorization, detection, and semantic segmentation. Within several years after the emergence of AlexNet, the performance of deep neural networks has already surpassed human being experts in certain areas and showed great potential in applications such as medical image analysis. The development of automated breast cancer detection systems that integrate deep learning has received wide attention from the community. Breast cancer, a major killer of females that results in millions of deaths, can be controlled even be cured given that it is detected at an early stage with sophisticated systems. In this paper, we reviewed breast cancer diagnosis, detection, and segmentation computer-aided (CAD) systems based on state-of-the-art deep convolutional neural networks. The available data sets also indirectly determine CAD systems' performance, so we introduced and discussed the details of public data sets. The challenges remaining in CAD systems for breast cancer are discussed at the end of this paper. The highlights of this survey mainly come from three following aspects. First, we covered a wide range of the basics of breast cancer from imaging modalities to popular databases in the community; Second, we presented the key elements in deep learning to form the compactness for methods mentioned in reviewed papers; Third and lastly, the summative details in each reviewed paper are provided so that interested readers can have a refined version of these works without referring to original papers.Therefore, this systematic survey suits readers with varied backgrounds and will be beneficial to them.

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Evolving the pulmonary nodules diagnosis from classical approaches to deep learning-aided decision support: three decades’ development course and future prospect

Liu

Chi

et al. 2019

J Cancer Res Clin Oncol

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Highlights► It covers the literature of the past thirty years' development in computer-assisted diagnosis of lung nodules.► A summary of algorithms from classical approaches to deep learning on pulmonary nodules diagnosis is provided.► Challenges and opportunities in learning models, algorithms and schemes are highlighted. AbstractLung cancer is the commonest cause of cancer deaths worldwide, and its mortality can be reduced significantly by performing early diagnosis and screening. Since the 1960s, driven by the pressing needs to accurately and effectively interpret the massive volume of chest images generated daily, computer-assisted diagnosis of pulmonary nodule has opened up new opportunities to relax the limitation from physicians' subjectivity, experiences and fatigue. And the fair access to the reliable and affordable computer-assisted diagnosis will fight the inequalities in incidence and mortality between populations. It has been witnessed that significant and remarkable advances have been achieved since the 1980s, and consistent endeavors have been exerted to deal with the grand challenges on how to accurately detect the pulmonary nodules with high sensitivity at low false-positives rate as well as on how to precisely differentiate between benign and malignant nodules. The main goal of this investigation is to provide a comprehensive state-of-the-art review of the computer-assisted nodules detection and benign-malignant classification techniques developed over three decades, which have evolved from the complicated ad hoc analysis pipeline of conventional approaches to the simplified seamlessly integrated deep learning techniques. This review also identifies challenges and highlights opportunities for future work in learning models, learning algorithms and enhancement schemes for bridging current state to future prospect and satisfying future demand. As far as the authors know, it is the first review of the literature of the past thirty years' development in computer-assisted diagnosis of lung nodules. We acknowledge the value of potential multidisciplinary researches that will make the computer-assisted diagnosis of pulmonary nodules enter into the main stream of clinical medicine, and raise the state-of-the-art clinical applications as well as increase both welfares of physicians and patients.

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Untitled

Cited by 17 publications

References 25 publications

Multiple neural networks modeling techniques in process control: a review

Multiple neural networks modeling techniques in process control: a review

A systematic survey of deep learning in breast cancer

Evolving the pulmonary nodules diagnosis from classical approaches to deep learning-aided decision support: three decades’ development course and future prospect

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