An Automatic Convolutional Neural Network Optimization Using a Diversity-Guided Genetic Algorithm

Fatyanosa, Tirana Noor; Aritsugi, Masayoshi

doi:10.1109/access.2021.3091729

Cited by 8 publications

(3 citation statements)

References 52 publications

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“…One of the biggest disadvantages of genetic algorithm is that it usually becomes stuck in a local optimal value and, as a result, results in yielding early convergence and non-optimal solutions [ 39 ]. Therefore, hyperparameter optimization techniques which benefit from genetic algorithm–based approaches are also likely to be problematic.…”

Section: Related Workmentioning

confidence: 99%

COVID-CCD-Net: COVID-19 and colon cancer diagnosis system with optimized CNN hyperparameters using gradient-based optimizer

Kiziloluk

Sert

2022

Med Biol Eng Comput

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Coronavirus disease-2019 (COVID-19) is a new types of coronavirus which have turned into a pandemic within a short time. Reverse transcription–polymerase chain reaction (RT-PCR) test is used for the diagnosis of COVID-19 in national healthcare centers. Because the number of PCR test kits is often limited, it is sometimes difficult to diagnose the disease at an early stage. However, X-ray technology is accessible nearly all over the world, and it succeeds in detecting symptoms of COVID-19 more successfully. Another disease which affects people’s lives to a great extent is colorectal cancer. Tissue microarray (TMA) is a technological method which is widely used for its high performance in the analysis of colorectal cancer. Computer-assisted approaches which can classify colorectal cancer in TMA images are also needed. In this respect, the present study proposes a convolutional neural network (CNN) classification approach with optimized parameters using gradient-based optimizer (GBO) algorithm. Thanks to the proposed approach, COVID-19, normal, and viral pneumonia in various chest X-ray images can be classified accurately. Additionally, other types such as epithelial and stromal regions in epidermal growth factor receptor (EFGR) colon in TMAs can also be classified. The proposed approach was called COVID-CCD-Net. AlexNet, DarkNet-19, Inception-v3, MobileNet, ResNet-18, and ShuffleNet architectures were used in COVID-CCD-Net, and the hyperparameters of this architecture was optimized for the proposed approach. Two different medical image classification datasets, namely, COVID-19 and Epistroma, were used in the present study. The experimental findings demonstrated that proposed approach increased the classification performance of the non-optimized CNN architectures significantly and displayed a very high classification performance even in very low value of epoch. Graphical abstract

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Section: Related Workmentioning

confidence: 99%

COVID-CCD-Net: COVID-19 and colon cancer diagnosis system with optimized CNN hyperparameters using gradient-based optimizer

Kiziloluk

Sert

2022

Med Biol Eng Comput

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“…CNN is superior performance in computer vision, especially in image recognition [1]- [3]. Furthermore, CNN benefits from high computation [4], a dominant deep learning technique [5], and rich hyperparameter [6], [7], [8] as an advantage of CNN.…”

Section: Introductionmentioning

confidence: 99%

“…level of knowledge in deep learning [7], the process of trial and error is tedious and long-time [11]. The solution to this problem is a hyperparameter automated approach.…”

Section: Introductionmentioning

confidence: 99%

Improving convolutional neural network based on hyperparameter optimization using variable length genetic algorithm for english digit handwritten recognition

Munsarif

Noersasongko²,

Andono³

et al. 2023

Int. J. Adv. Intell. Informatics

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Convolutional Neural Networks (CNNs) perform well compared to other deep learning models in image recognition, especially in handwritten alphabetic numeral datasets. CNN's challenging task is to find an architecture with the right hyperparameters. Usually, this activity is done by trial and error. A genetic algorithm (GA) has been widely used for automatic hyperparameter optimization. However, the original GA with fixed chromosome length allows for suboptimal solution results because CNN has a variable number of hyperparameters depending on the depth of the model. Previous work proposed variable chromosome lengths to overcome the drawbacks of native GA. This paper proposes a variable length GA by adding global hyperparameters, namely optimizer and learning speed, to systematically and automatically tune CNN hyperparameters to improve performance. We optimize seven hyperparameters, such as the learning rate. Optimizer, kernel, filter, activation function, number of layers and pooling. The experimental results show that a population of 25 produces the best fitness value and average fitness. In addition, the comparison results show that the proposed model is superior to the basic model based on accuracy. The experimental results show that the proposed model is about 99.18% higher than the baseline model.

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Applications in Medical Technology for Optimized Convolutional Neural Network Using Differential Evolutionary Algorithm

Buradkar,

Ambhaikar

2023

Lecture Notes in Electrical Engineering

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An Automatic Convolutional Neural Network Optimization Using a Diversity-Guided Genetic Algorithm

Cited by 8 publications

References 52 publications

COVID-CCD-Net: COVID-19 and colon cancer diagnosis system with optimized CNN hyperparameters using gradient-based optimizer

COVID-CCD-Net: COVID-19 and colon cancer diagnosis system with optimized CNN hyperparameters using gradient-based optimizer

Improving convolutional neural network based on hyperparameter optimization using variable length genetic algorithm for english digit handwritten recognition

Applications in Medical Technology for Optimized Convolutional Neural Network Using Differential Evolutionary Algorithm

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