A Novel Active Semisupervised Convolutional Neural Network Algorithm for SAR Image Recognition

Gao, Fei; Yue, Zhenyu; Wang, Jun; Sun, Jinping; Yang, Erfu; Zhou, Huiyu

doi:10.1155/2017/3105053

Cited by 40 publications

(21 citation statements)

References 21 publications

(24 reference statements)

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“…One of the most powerful deep networks is the convolutional neural network that can include multiple hidden layers performing convolution and subsampling in order to extract low to high levels of features of the input data [ 27 – 30 ]. This network has shown a great efficiency in different areas, particularly, in computer vision [ 28 ], biological computation [ 29 ], fingerprint enhancement [ 30 ], and so on. Basically, this type of networks consists of three layers: convolution layers, subsampling or pooling layers, and full connection layers.…”

Section: Machine Learning For Diagnosis Of Chest Diseasesmentioning

confidence: 99%

Deep Convolutional Neural Networks for Chest Diseases Detection

Abiyev

Ma’aitah

2018

Journal of Healthcare Engineering

264

118

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Chest diseases are very serious health problems in the life of people. These diseases include chronic obstructive pulmonary disease, pneumonia, asthma, tuberculosis, and lung diseases. The timely diagnosis of chest diseases is very important. Many methods have been developed for this purpose. In this paper, we demonstrate the feasibility of classifying the chest pathologies in chest X-rays using conventional and deep learning approaches. In the paper, convolutional neural networks (CNNs) are presented for the diagnosis of chest diseases. The architecture of CNN and its design principle are presented. For comparative purpose, backpropagation neural networks (BPNNs) with supervised learning, competitive neural networks (CpNNs) with unsupervised learning are also constructed for diagnosis chest diseases. All the considered networks CNN, BPNN, and CpNN are trained and tested on the same chest X-ray database, and the performance of each network is discussed. Comparative results in terms of accuracy, error rate, and training time between the networks are presented.

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Section: Machine Learning For Diagnosis Of Chest Diseasesmentioning

confidence: 99%

Deep Convolutional Neural Networks for Chest Diseases Detection

Abiyev

Ma’aitah

2018

Journal of Healthcare Engineering

264

118

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“…The CNN architectures use the convolution and pooling operations to extract low to higher levels of features in the input data. The CNN has been used mostly for image-based analysis [54,57] although the CNN has found other application areas which include biological, health, face and finger recognition systems [15,44,55]. These layers are explained as follows.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Achieving remanufacturing inspection using deep learning

et al. 2020

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Deep learning has emerged as a state-of-the-art learning technique across a wide range of applications, including image recognition, object detection and localisation, natural language processing, prediction and forecasting systems. With significant applicability, deep learning could be used in new and broader areas of applications, including remanufacturing. Remanufacturing is a process of taking used products through disassembly, inspection, cleaning, reconditioning, reassembly and testing to ascertain that their condition meets new products conditions with warranty. This process is complex and requires a good understanding of the respective stages for proper analysis. Inspection is a critical process in remanufacturing, which guarantees the quality of the remanufactured products. It is currently an expensive manual operation in the remanufacturing process that depends on operator expertise, in most cases. This research investigates the application of deep learning algorithms to inspection in remanufacturing, towards automating the inspection process. This paper presents a novel vision-based inspection system based on deep convolution neural network (DCNN) for eight types of defects, namely pitting, rust, cracks and other combination faults. The materials used for this feasibility study were 100 cm × 150 cm mild steel plate material, purchased locally, and captured using a USB webcam of 0.3 megapixels. The performance of this preliminary study indicates that the DCNN can classify with up to 100% accuracy on validation data and above 96% accuracy on a live video feed, by using 80% of the sample dataset for training and the remaining 20% for testing. Therefore, in the remanufacturing parts inspection, the DCNN approach has high potential as a method that could surpass the current technologies used in the design of inspection systems. This research is the first to apply deep learning techniques in remanufacturing inspection. The proposed method offers the potential to eliminate expert judgement in inspection, save cost, increase throughput and improve precision. This preliminary study demonstrates that deep learning techniques have the potential to revolutionise inspection in remanufacturing. This research offers valuable insight into these opportunities, serving as a starting point for future applications of deep learning algorithms to remanufacturing.

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“…Peng et al [8] proposed a new CNN architecture with spatial pyramid pooling which can build high hierarchy of features maps by dividing the convoluted feature maps from finer to coarser levels of aggregate local features of SAR images. Gao et al [9] proposed a new effective semi‐supervised CNN algorithm which can reduce the dependence of a large amount of labelled samples. Although these models have achieved better results, the parameter estimation of these algorithms is very complicated.…”

Section: Related Workmentioning

confidence: 99%