A review on deep convolutional neural networks

Aloysius, Neena; Geetha, M. Kalaiselvi

doi:10.1109/iccsp.2017.8286426

Cited by 448 publications

(239 citation statements)

References 9 publications

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“…In this study, one of the most general types of CNN, containing a convolution layer, a pooling layer, and a fully-connected layer, was adopted [21,24]. To perform CNN regression and estimate the Brix/acid ratio, the regression layer was put in the final layer.…”

Section: Estimation Of the Brix/acid Ratio With Cnn Regressionmentioning

confidence: 99%

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Estimation of Citrus Maturity with Fluorescence Spectroscopy Using Deep Learning

et al. 2018

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To produce high-quality citrus, the harvest time of citrus should be determined by considering its maturity. To evaluate citrus maturity, the Brix/acid ratio, which is the ratio of sugar content or soluble solids content to acid content, is one of the most commonly used indicators of fruit maturity. To estimate the Brix/acid ratio, fluorescence spectroscopy, which is a rapid, sensitive, and cheap technique, was adopted. Each citrus peel was extracted, and its fluorescence value was measured. Then, the fluorescent spectrum was analyzed using a convolutional neural network (CNN). In fluorescence spectroscopy, a matrix called excitation and emission matrix (EEM) can be obtained, in which each fluorescence intensity was recorded at each excitation and emission wavelength. Then, by regarding the EEM as an image, the Brix/acid ratio of juice from the flesh was estimated via performing a regression with a CNN (CNN regression). As a result, the Brix/acid ratio absolute error was estimated to be 2.48, which is considerably better than the values obtained by the other methods in previous studies. Hyperparameters, such as depth of layers, learning rate, and the number of filters used for this estimation, could be observed using Bayesian optimization, and the optimization contributed to the high accuracy.

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Section: Estimation Of the Brix/acid Ratio With Cnn Regressionmentioning

confidence: 99%

“…The optimal parameters change according to the sample and the objective. It is difficult, expensive, and time-consuming to construct good architecture of a network [20,21].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Citrus Maturity with Fluorescence Spectroscopy Using Deep Learning

et al. 2018

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“…Also the study of regularization functions has been of aid in avoiding the overfitting of the Networks in training [21,22]. For a review of the state of the art in Convolutional Neural Networks the reader may review the works of: [12,15,23,24].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Transfer Learning and Fine Tuning in Breast Mammogram Abnormalities Classification on CBIS-DDSM Database

Falconi¹,

Pérez²,

Aguila³

et al. 2020

Adv. sci. technol. eng. syst. j.

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Breast cancer has an important incidence in women mortality worldwide. Currently, mammography is considered the gold standard for breast abnormalities screening examinations, since it aids in the early detection and diagnosis of the illness. However, both identification of mass lesions and its malignancy classification is a challenging problem for artificial intelligence. In this work, we extend our previous research in mammogram classification, where we studied NasNet and MobileNet in transfer learning to train a breast abnormality malignancy classifier, and include models like: VGG, Resnet, Xception and Resnext. However, training deep learning models tends to overfit. This problem is also carried out in this work. Our results show that Fine Tuning achieves the best classifier performance in VGG16 with AUC value of 0.844 in the CBIS-DDSM dataset.

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“…In starting, A simple model LENET [16]is used [13].There are as non linearity constraint.2 FCC layer is employed having softmax output of 43.Gradient Descent is used as a method to reduce loss. This model produces following accuracy…”

Section: Model Architecturementioning

confidence: 99%

Autonomous Decision Making for a Vehicle

Isha¹,

Mamtesh²

2019

IJCA

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It includes Deep Learning techniques using convolutional neural networks for getting the predictions and probabilities for the best decision that has to be made while driving a car like lane detection, traffic signals recognition and their localization and simultaneously developing a steering model to help to take decision regarding steering wheel, throttle thus stimulate a car like humans. In this paper, lane detection is the main concern is to move the steering in appropriate direction with proper angle and the problem is solved using convolutional neural networks via creating a model which then is trained over some collected training data of steering decisions on a track in a simulated environment.

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A review on deep convolutional neural networks

Cited by 448 publications

References 9 publications

Estimation of Citrus Maturity with Fluorescence Spectroscopy Using Deep Learning

Estimation of Citrus Maturity with Fluorescence Spectroscopy Using Deep Learning

Transfer Learning and Fine Tuning in Breast Mammogram Abnormalities Classification on CBIS-DDSM Database

Autonomous Decision Making for a Vehicle

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