OptCoNet: an optimized convolutional neural network for an automatic diagnosis of COVID-19

Goel, Tripti; Murugan, R.; Mirjalili, Seyedali; Chakrabartty, Deba Kumar

doi:10.1007/s10489-020-01904-z

Cited by 145 publications

(107 citation statements)

References 32 publications

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“…However, the choice of parameters is mainly standard. The optimization methodology adopted in [9] , is somewhat similar to the proposed design, however, the hyper parameter optimization methodology of our proposed design is unique. In contrast to the methodology reported in [9] , the proposed design has resulted in improved performance for both datasets.…”

Section: Resultsmentioning

confidence: 99%

“…The optimization methodology adopted in [9] , is somewhat similar to the proposed design, however, the hyper parameter optimization methodology of our proposed design is unique. In contrast to the methodology reported in [9] , the proposed design has resulted in improved performance for both datasets. Further, in [9] , the optimization was limited to a single dataset, whereas the universal acceptability of optimized parameters is proved by our proposed methodology since the same optimization parameters hold true for dataset 2 resulting in good classification performance.…”

Section: Resultsmentioning

confidence: 99%

“…The features are extracted from the fully connected layer comprising of 1000 nodes. Rather, than optimizing the parameters of deep neural network architecture as in [9] , we propose a novel strategy by introducing a Multilayer Perceptron (MLP) based GWO optimization.…”

Section: Methodsmentioning

confidence: 99%

“… [8] used 224 covid-19 images and obtained a class accuracy of 93.48%. An optimized CNN termed as optconet was proposed in [9] using a total of 2700 images thereby yielding a precision score of 92.8%. A mobilenet CNN model was developed by Apostolopoulous et al.…”

Section: Introductionmentioning

confidence: 99%

“… Two optimization algorithms namely GWO and WOA-BAT were used for tuning the hyper parameters of CNN. In contrast to the method in [9] , the proposed method determines the values of the hyper parameters by optimizing an MLP trained on the features extracted from the ResNet-50. The best values obtained were used as hyper parameters (Initial Learning Rate, L2 regularization, gradient decay factor, and Maximum epochs).…”

Section: Introductionmentioning

confidence: 99%

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Automated Detection of Covid-19 from Chest X-ray scans using an optimized CNN architecture

Pathan

Siddalingaswamy

Ali

2021

Applied Soft Computing

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The novel coronavirus termed as covid-19 has taken the world by its crutches affecting innumerable lives with devastating impact on the global economy and public health. One of the major ways to control the spread of this disease is identification in the initial stage, so that isolation and treatment could be initiated. Due to the lack of automated auxiliary diagnostic medical tools, availability of lesser sensitivity testing kits, and limited availability of healthcare professionals, the pandemic has spread like wildfire across the world. Certain recent findings state that chest X-ray scans contain salient information regarding the onset of the virus, the information can be analyzed so that the diagnosis and treatment can be initiated at an earlier stage. This is where artificial intelligence meets the diagnostic capabilities of experienced clinicians. The objective of the proposed research is to contribute towards fighting the global pandemic by developing an automated image analysis module for identifying covid-19 affected chest X-ray scans by employing an optimized Convolution Neural Network (CNN) model. The aforementioned objective is achieved in the following manner by developing three classification models, (i) ensemble of ResNet 50-Error Correcting Output Code (ECOC) model, (ii) CNN optimized using Grey Wolf Optimizer (GWO) and, (iii) CNN optimized using Whale Optimization + BAT algorithm. The novelty of the proposed method lies in the automatic tuning of hyper parameters considering a hierarchy of MultiLayer Perceptron (MLP), feature extraction, and optimization ensemble. A 100% classification accuracy was obtained in classifying covid-19 images. Classification accuracy of 98.8% and 96% were obtained for dataset 1 and dataset 2 respectively for classification into covid-19, normal, and viral pneumonia cases. The proposed method can be adopted in a clinical setting for assisting radiologists and it can also be employed in remote areas to facilitate the faster screening of affected patients.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automated Detection of Covid-19 from Chest X-ray scans using an optimized CNN architecture

Pathan

Siddalingaswamy

Ali

2021

Applied Soft Computing

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Characterization of abnormalities in breast cancer images using nature‐inspired metaheuristic optimized convolutional neural networks model

Oyelade

Ezugwu

2021

Concurrency and Computation

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Convolutional neural networks (CNN) are deep learning models widely reported as performing well in several image classification tasks. Training the networks for optimal performance is considered an NP-hard problem. The combination of a metaheuristic algorithm with CNN has been proposed to address this problem because the metaheuristic algorithms have efficient performance, parameter tuning, and stagnation prevention methods. Moreover, there is now an increasing need to apply the best performing metaheuristic algorithm to optimize the parameters, training and learning rate of the CNN models. This study aims to investigate the best performing metaheuristic algorithm for fine-tuning the weights, biases, and hyperparameters of CNN networks for solving the problem of characterization of abnormalities in breast images. Furthermore, hybrid models consisting of a CNN architecture and five representative metaheuristic algorithms to efficiently detect breast cancer abnormalities are presented. The adopted approach involves training a CNN network using genetic algorithm (GA), whale optimization algorithm (WOA), multiverse optimizer (MVO), satin bower optimization (SBO), and life choice-based optimization (LCBO) algorithms to optimize only weights and bias of the model. Two categories of experiments were carried out: the first involved training of the CNN model, while the second involved optimizing the training of CNN model with metaheuristic algorithms. A comparative analysis of the impact of the proposed GA, WOA, MVO, SBO, and LCBO metaheuristic algorithms on the performance of the CNN architecture was carried out. Empirical and statistical analyses are presented to validate further the findings obtained in the study. Results obtained showed that MVO, SBO and LCBO outperformed GA, WOA. The classification accuracy obtained for CNN-GA, CNN-WOA, CNN-MVO, CNN-SBO, and CNN-LCBO at the fifth epoch was 0.76, 0.75, 0.84, 0.80, and 0.86, respectively. At the same time, the traditional CNN achieved an accuracy of 0.66. The outcome of this study showed that contrary to widespread practice, physics-based, biology-based, and human-based optimization algorithms promise better performance parameter tuning for CNN as compared with evolutionary-based and swarm-based metaheuristic algorithms.

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Identification of the types of disease for tomato plants using a modified gray wolf optimization optimized MobileNetV2 convolutional neural network architecture driven computer vision framework

Mukherjee¹,

Chatterjee

Tudu

2022

Concurrency and Computation

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SUMMARY Tomato is a widely consumed fruit across the world due to its high nutritional values. Leaf diseases in tomato are very common which incurs huge damages but early detection of leaf diseases can help in avoiding that. The existing practices for detecting different diseases by the human experts are costly, time consuming and subjective in nature. Computer vision plays important role toward early detection of tomato leaf detection. However, implementation of computationally less expensive model and improvement of detection performance is still open. This article reports a computer vision based system to classify seven different categories of diseases, namely, bacterial spot, early blight, late blight, leaf mold, septoria leaf spot, spider mites, and target spots using optimized MobileNetV2 architecture. A modified gray wolf optimization approach has been adopted for optimization of MobileNetV2 hyperparameters for improved performance. The model has been validated using standard internal and external validation methods and found to provide the classification accuracy in the tune of 98%. The results reflect the promising potential of the presented framework for early detection of tomato leaf diseases which can help to avoid substantial agricultural loss.

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OptCoNet: an optimized convolutional neural network for an automatic diagnosis of COVID-19

Cited by 145 publications

References 32 publications

Automated Detection of Covid-19 from Chest X-ray scans using an optimized CNN architecture

Automated Detection of Covid-19 from Chest X-ray scans using an optimized CNN architecture

Characterization of abnormalities in breast cancer images using nature‐inspired metaheuristic optimized convolutional neural networks model

Identification of the types of disease for tomato plants using a modified gray wolf optimization optimized MobileNetV2 convolutional neural network architecture driven computer vision framework

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