Hyper-Parameter Selection in Convolutional Neural Networks Using Microcanonical Optimization Algorithm

Gülcü, Ayla; Kuş, Zeki

doi:10.1109/access.2020.2981141

Cited by 28 publications

(10 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CNN architectures used in computer vision and image processing are a deep learning approach applied to data exhibiting similar topology, such as sequential pixels in images (Goodfellow, Bengio, & Courville, 2017). The serious processing complexity encountered by other neural network models in image processing is overcome with CNN models and reduced to more manageable states (Ankile, Heggland, & Krange, 2020).…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Covid-19 Detection from Chest X-Ray Images and Hybrid Model Recommendation with Convolutional Neural Networks

Karacan

ERYILMAZ²

2021

Journal of Advanced Research in Natural and Applied Sciences

View full text Add to dashboard Cite

The coronavirus pandemic, which emerged at the end of 2019, continues to be effective. Although various vaccines have been developed, the diagnosis of the disease and the 14-day isolation method still remain valid. Purpose: To classify diseases such as COVID-19, tuberculosis, other pneumonias, and lung opacity. For this goal, it was aimed to automate disease diagnosis, which was done manually by expert radiologists with chest X-Ray images, with a hybrid model created with the four most commonly used convolutional neural networks. In addition, it is aimed to reach a more accurate result by combining four CNN models instead of one model during the epidemic when diagnosis is of critical importance. Method: In the binary classification, the images were categorised as COVID-19positive and negative, and multi-class classification was conducted according to labels including Lung Opacity, COVID-19, Normal, and Viral Pneumonia. The recommended model consisted of MobileNetV2, DenseNet121, InceptionResNetV2, and Xception networks. Transfer learning was used for initial weights. These models, were combined with ensemble learning to obtain better classification performance. Results: The best binary classification result was obtained from the MobileNetv2 with an accuracy rate of 98.84%. The hybrid model improved slightly, with a value 0.16. In the multi-class classification, the DenseNet121 accomplished the highest classification rate, with 93.17%. The hybrid model improvement in the multi-class classification was 0.81. According to these results, the proposed method will alleviate the burden of health personnel during the epidemic. It will also aid in the diagnosis of COVID-19 in areas where facilities are limited.

show abstract

Section: Convolutional Neural Networkmentioning

confidence: 99%

Covid-19 Detection from Chest X-Ray Images and Hybrid Model Recommendation with Convolutional Neural Networks

Karacan

ERYILMAZ²

2021

Journal of Advanced Research in Natural and Applied Sciences

View full text Add to dashboard Cite

show abstract

Section: Proposed Cnn Modelsmentioning

confidence: 99%

“…The selection of such hyperparameters is a tedious and time-consuming process. However, recently, different techniques have been introduced to choose the most suitable hyperparameter group for the solution of a problem (11,14). While making the model design, the first choices we make for hyperparameters generally do not lead us to the correct results.…”

Section: Proposed Cnn Modelsmentioning

confidence: 99%

Implementation of convolutional neural network approach for COVID-19 disease detection

Irmak

2020

Physiological Genomics

View full text Add to dashboard Cite

In this paper two novel, powerful and robust Convolutional Neural Network (CNN) architectures are designed and proposed for two different classification tasks using publicly available datasets. The first architecture is able to decide whether a given chest X-ray image of a patient contains COVID-19 or not with 98.92% average accuracy. The second CNN architecture is able to divide a given chest X-ray image of a patient into three classes (COVID-19 vs. Normal vs. Pneumonia) with 98.27% average accuracy. The hyper-parameters of the both CNN models are automatically determined using Grid Search. Experimental results on large clinical datasets show the effectiveness of the proposed architectures and demonstrate that the proposed algorithms can overcome disadvantages mentioned above. Moreover, the proposed CNN models are fully-automatic in terms of not requiring the extraction of diseased tissue; which is a great improvement of available automatic methods in the literature. To the best of author's knowledge, this study is the first study to detect COVID-19 disease from given chest X-ray images, using CNN whose hyper parameters are automatically determined by the Grid Search. Another important contribution of this study is that it is the first CNN based COVID-19 chest X-ray image classification study which uses the largest possible clinical dataset. A total of 1524 COVID-19, 1527 pneumonia and 1524 normal X-ray images are collected. It is aimed to collect the largest number of COVID-19 X-ray images that exist in the literature until the writing of this research paper.

show abstract

“…There are many research works on hyper-parameter tuning of machine learning models, including convolutional neural networks (CNN), etc. [1]. At present, neural network models have made remarkable achievements in the fields of image recognition,fault detection and classification (FDC) [2][3][4], natural language processing, and so on.…”

Section: Introductionmentioning

confidence: 99%

A New Hyper-Parameter Optimization Method for Power Load Forecast Based on Recurrent Neural Networks

Zhang

Cai

2021

Algorithms

View full text Add to dashboard Cite

The selection of the hyper-parameters plays a critical role in the task of prediction based on the recurrent neural networks (RNN). Traditionally, the hyper-parameters of the machine learning models are selected by simulations as well as human experiences. In recent years, multiple algorithms based on Bayesian optimization (BO) are developed to determine the optimal values of the hyper-parameters. In most of these methods, gradients are required to be calculated. In this work, the particle swarm optimization (PSO) is used under the BO framework to develop a new method for hyper-parameter optimization. The proposed algorithm (BO-PSO) is free of gradient calculation and the particles can be optimized in parallel naturally. So the computational complexity can be effectively reduced which means better hyper-parameters can be obtained under the same amount of calculation. Experiments are done on real world power load data,where the proposed method outperforms the existing state-of-the-art algorithms,BO with limit-BFGS-bound (BO-L-BFGS-B) and BO with truncated-newton (BO-TNC),in terms of the prediction accuracy. The errors of the prediction result in different models show that BO-PSO is an effective hyper-parameter optimization method.

show abstract

Hyper-Parameter Selection in Convolutional Neural Networks Using Microcanonical Optimization Algorithm

Cited by 28 publications

References 37 publications

Covid-19 Detection from Chest X-Ray Images and Hybrid Model Recommendation with Convolutional Neural Networks

Covid-19 Detection from Chest X-Ray Images and Hybrid Model Recommendation with Convolutional Neural Networks

Implementation of convolutional neural network approach for COVID-19 disease detection

A New Hyper-Parameter Optimization Method for Power Load Forecast Based on Recurrent Neural Networks

Contact Info

Product

Resources

About