A novel training algorithm for convolutional neural network

Anuse, Alwin; Vyas, Vibha

doi:10.1007/s40747-016-0024-6

Cited by 50 publications

(21 citation statements)

References 78 publications

(66 reference statements)

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“…With the advancements in healthcare image processing methods, there is a drastic increase observed in prediction and diagnostic devices [7]. ML methods are broadly known as projected tools to improve the diagnostic and prediction processes of numerous diseases [8]. Though effective feature extraction methods [9] are required to attain efficient ML techniques, DL is an extensive method which is approved in healthcare image system, thanks to its automated extraction feature like ResNet.…”

Section: Related Workmentioning

confidence: 99%

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A novel hand-crafted with deep learning features based fusion model for COVID-19 diagnosis and classification using chest X-ray images

Shankar

Perumal

2020

Complex Intell. Syst.

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COVID-19 pandemic is increasing in an exponential rate, with restricted accessibility of rapid test kits. So, the design and implementation of COVID-19 testing kits remain an open research problem. Several findings attained using radio-imaging approaches recommend that the images comprise important data related to coronaviruses. The application of recently developed artificial intelligence (AI) techniques, integrated with radiological imaging, is helpful in the precise diagnosis and classification of the disease. In this view, the current research paper presents a novel fusion model hand-crafted with deep learning features called FM-HCF-DLF model for diagnosis and classification of COVID-19. The proposed FM-HCF-DLF model comprises three major processes, namely Gaussian filtering-based preprocessing, FM for feature extraction and classification. FM model incorporates the fusion of handcrafted features with the help of local binary patterns (LBP) and deep learning (DL) features and it also utilizes convolutional neural network (CNN)-based Inception v3 technique. To further improve the performance of Inception v3 model, the learning rate scheduler using Adam optimizer is applied. At last, multilayer perceptron (MLP) is employed to carry out the classification process. The proposed FM-HCF-DLF model was experimentally validated using chest X-ray dataset. The experimental outcomes inferred that the proposed model yielded superior performance with maximum sensitivity of 93.61%, specificity of 94.56%, precision of 94.85%, accuracy of 94.08%, F score of 93.2% and kappa value of 93.5%.

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Section: Related Workmentioning

confidence: 99%

“…Figs. 6,7,8,9 show the results of analysis conducted upon FM-HCF-DLF model in terms of diverse measures under varying numbers of folds.…”

mentioning

confidence: 99%

A novel hand-crafted with deep learning features based fusion model for COVID-19 diagnosis and classification using chest X-ray images

Shankar

Perumal

2020

Complex Intell. Syst.

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“…This common information is used to convey knowledge that is gained from the face domain and classify the nose or lip biometric. Use of transfer learning has been made in multiple applications like kinship verification in photo [26], image annotation [27], brain decoding in the medical field [32], face recognition [29,31], cross-domain age estimation [28] and cross-domain association [30]. Transfer learning using dual codebook has been proposed for cross-view action recognition and it shows that it works better compared to stateof-the-art methods [33].…”

Section: Related Workmentioning

confidence: 99%

Association of Face and Facial Components Based on CNN and Transfer Subspace Learning for Forensics Applications

2020

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Face and facial components are the most significant pieces of evidence used in forensic applications. Component-based recognition and age-invariant face recognition are the key factors in forensic face recognition. A major challenge during a forensic investigation is the association of facial components with a relevant face and this paper addresses this concern using the proposed transfer learning methodology. Research has been carried out treating facial components and the face as individual and separate entities. However, they share common auxiliary information which is exploited to bring the face and facial components in a common subspace in the proposed method. This facilitates transfer of knowledge gained from a face and classification of facial components of a person using the same subspace. Stability and invariant features of the facial components catalyze a solution for the problem of recognition. Convolutional neural network (CNN) is used to extract the features of the biometrics and then they are given to a regularization framework for reducing the probability distribution difference between them. The system is trained using the face and facial components and then tested using either the full-face or individual facial components. For the transfer, the proposed Fisher linear discriminant analysis and locality preserving projection, a convolutional neural network-based algorithm gives 91% and 90% accuracy, respectively, which outperforms the Histogram of Gradient and Gabor methods for predicting an association.

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“…One deeplearning image detection algorithm and its variants, which stands out of the rest and are used in most image recognition systems, are the CNN and its variants. The CNN has enhanced image feature extraction capabilities that allow it to achieve advanced levels of image recognition [23][24][25]. In this paper, we propose an improved NILM image disaggregation framework that is based on the staked denoising autoencoder (sdAE) using CNN layers.…”

Section: Introductionmentioning

confidence: 99%

Image-based mains signal disaggregation and load recognition

Matindife

Sun

Wang

2021

Complex Intell. Syst.

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The mains signal is a complex fusion of various electrical equipment load signals in a building. In the non-intrusive load monitoring recognition, our main aim is to be able to extract as much load features as possible from the complex aggregate mains signal in a simpler way through a computer vision-based approach as opposed to the powers series signal approach. Power series methods, which are one dimensional in nature, suffer from poor aggregate and load signal feature localization necessitating a larger training dataset spanning very long time periods and normally require signal formatting and pre-processing. We use Gramian angular summation fields to transform the power series into a reduced image dataset that contains a rich set of localized signal features. A computer vision approach allows us to capture as much information as possible, and then propose an image-based mains load recognition system with high performance. In this paper for the entire recognition system, we use convolutional neural networks that very well adapted to vision recognition. The load signal image disaggregation is achieved through the powerful stacked denoising autoencoder noise extraction network. To test the proposed system, some simulations and comparisons are carried out and the results show that our easier to handle method can achieve acceptable performance.

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A novel training algorithm for convolutional neural network

Cited by 50 publications

References 78 publications

A novel hand-crafted with deep learning features based fusion model for COVID-19 diagnosis and classification using chest X-ray images

A novel hand-crafted with deep learning features based fusion model for COVID-19 diagnosis and classification using chest X-ray images

Association of Face and Facial Components Based on CNN and Transfer Subspace Learning for Forensics Applications

Image-based mains signal disaggregation and load recognition

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