Method that determining the Hyperparameter of CNN using HS algorithm

Lee, Wooyoung; Ko, Kwang-Eun; Geem, Zong-Woo; Sim, Kwee-Bo

doi:10.5391/jkiis.2017.27.1.022

Cited by 9 publications

(6 citation statements)

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“…Optimizing hyperparameters to achieve the expected results the work must be preceded. But so far, the hyperparameter It is common that there are no rules for optimizing and hyperparameters when making a decision, much of it must be made based on experience or the designer's intuition [15].…”

Section: Figure 1 Cnn Methods Layer Sequencesmentioning

confidence: 99%

Application of Deep Learning Using Convolutional Neural Network (CNN) Method For Women’s Skin Classification

Anton

Nissa

Janiati

et al. 2021

SJI

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Facial skin is skin that protects the inside of the face such as the eyes, nose, mouth, and others. Facial skin consists of several types, including normal skin, oily skin, dry skin, and combination skin. This is a problem for women because it is difficult to recognize and distinguish their skin types this is what causes some women to find it difficult to determine the right make-up and care products for their skin types. In this study, the Convolutional Neural Network (CNN) method is the right method for classifying women's skin types from the age of 20-30 years by following several stages using Python 3.5 programming with a depth of three layers and the results of this research using the CNN method get the results of the accuracy value good at 67%

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Section: Figure 1 Cnn Methods Layer Sequencesmentioning

confidence: 99%

Application of Deep Learning Using Convolutional Neural Network (CNN) Method For Women’s Skin Classification

Anton

Nissa

Janiati

et al. 2021

SJI

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show abstract

“…The relationship between high and low resolutions has been discovered in the same image. In particular, as shown in Figure 1, a convolutional neural network CNN [9], a very deep super-resolution (VDSR) [10], and a road structure refined CNN (RSRCNN) [13], and a deeply-recursive convolutional network (DRCN) [14], which are based on CNN [15][16][17], performed better than existing networks. The difference between a low-resolution image obtained using an existing interpolation method and a high-resolution image obtained from these models is shown in Figure 2 [9].…”

Section: Related Workmentioning

confidence: 99%

A Restoration Method of Single Image Super Resolution Using Improved Residual Learning with Squeeze and Excitation Blocks

Jeon¹,

Rhee²

2021

IJFIS

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Techniques for single-image super-resolution have been developed through deep learning. In this paper, we propose a method using advanced residual learning and squeeze and excitation (SE) blocks for such resolution. Improving the residual learning increases the similarity between pixels by adding one skip to the existing residual, and it is possible to improve the performance while slightly increasing the number of calculations by applying the SE block of SENet. The performance evaluation was tested as part of the super-resolution generative adversarial network (SRGAN) and using three proposed modules, and the effect of the residual and the SE blocks on the super-resolution and the change in performance was confirmed. Although the results vary slightly from image, SE and residual blocks have been found to help improve the performance and are best used with blocks.

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“…However, there are no standard rules for optimizing CNN hyperparameters that influence the feature extraction process. This has depended mostly on the designer's intuition [35,67].…”

Section: The Proposed Hybrid Lenet-5/abc Algorithm As Feature Extractor and Classifier Of Liver Lesionsmentioning

confidence: 99%

A Novel Bio-Inspired Deep Learning Approach for Liver Cancer Diagnosis

Ghoniem

2020

Information

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Current research on computer-aided diagnosis (CAD) of liver cancer is based on traditional feature engineering methods, which have several drawbacks including redundant features and high computational cost. Recent deep learning models overcome these problems by implicitly capturing intricate structures from large-scale medical image data. However, they are still affected by network hyperparameters and topology. Hence, the state of the art in this area can be further optimized by integrating bio-inspired concepts into deep learning models. This work proposes a novel bio-inspired deep learning approach for optimizing predictive results of liver cancer. This approach contributes to the literature in two ways. Firstly, a novel hybrid segmentation algorithm is proposed to extract liver lesions from computed tomography (CT) images using SegNet network, UNet network, and artificial bee colony optimization (ABC), namely, SegNet-UNet-ABC. This algorithm uses the SegNet for separating liver from the abdominal CT scan, then the UNet is used to extract lesions from the liver. In parallel, the ABC algorithm is hybridized with each network to tune its hyperparameters, as they highly affect the segmentation performance. Secondly, a hybrid algorithm of the LeNet-5 model and ABC algorithm, namely, LeNet-5/ABC, is proposed as feature extractor and classifier of liver lesions. The LeNet-5/ABC algorithm uses the ABC to select the optimal topology for constructing the LeNet-5 network, as network structure affects learning time and classification accuracy. For assessing performance of the two proposed algorithms, comparisons have been made to the state-of-the-art algorithms on liver lesion segmentation and classification. The results reveal that the SegNet-UNet-ABC is superior to other compared algorithms regarding Jaccard index, Dice index, correlation coefficient, and convergence time. Moreover, the LeNet-5/ABC algorithm outperforms other algorithms regarding specificity, F1-score, accuracy, and computational time.

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Method that determining the Hyperparameter of CNN using HS algorithm

Cited by 9 publications

References 0 publications

Application of Deep Learning Using Convolutional Neural Network (CNN) Method For Women’s Skin Classification

Application of Deep Learning Using Convolutional Neural Network (CNN) Method For Women’s Skin Classification

A Restoration Method of Single Image Super Resolution Using Improved Residual Learning with Squeeze and Excitation Blocks

A Novel Bio-Inspired Deep Learning Approach for Liver Cancer Diagnosis

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