Multiobjective Genetic Algorithm and Convolutional Neural Network Based COVID-19 Identification in Chest X-Ray Images

Shukla, Prashant Kumar; Sandhu, Jasminder Kaur; Ahirwar, Anamika; Ghai, Deepika; Maheshwary, Priti; Shukla, Piyush Kumar

doi:10.1155/2021/7804540

Cited by 136 publications

(62 citation statements)

References 31 publications

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“…Feature values extracted and recorded the values of these features in a feature matrix form. The next phase in the identification process is the conversion of feature matrix values to an understandable classifier format [ 27 ].…”

Section: Feature Extractionmentioning

confidence: 99%

Machine Learning Techniques for Human Age and Gender Identification Based on Teeth X-Ray Images

Santosh¹,

Pradeep²,

Goel³

et al. 2022

Journal of Healthcare Engineering

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The use of digital medical images is increasing with advanced computational power that has immensely contributed to developing more sophisticated machine learning techniques. Determination of age and gender of individuals was manually performed by forensic experts by their professional skills, which may take a few days to generate results. A fully automated system was developed that identifies the gender of humans and age based on digital images of teeth. Since teeth are a strong and unique part of the human body that exhibits least subject to risk in natural structure and remains unchanged for a longer duration, the process of identification of gender- and age-related information from human beings is systematically carried out by analyzing OPG (orthopantomogram) images. A total of 1142 digital X-ray images of teeth were obtained from dental colleges from the population of the middle-east part of Karnataka state in India. 80% of the digital images were considered for training purposes, and the remaining 20% of teeth images were for the testing cases. The proposed gender and age determination system finds its application widely in the forensic field to predict results quickly and accurately. The prediction system was carried out using Multiclass SVM (MSVM) classifier algorithm for age estimation and LIBSVM classifier for gender prediction, and 96% of accuracy was achieved from the system.

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Section: Feature Extractionmentioning

confidence: 99%

Machine Learning Techniques for Human Age and Gender Identification Based on Teeth X-Ray Images

Santosh¹,

Pradeep²,

Goel³

et al. 2022

Journal of Healthcare Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…In [18], the authors introduced a framework to classify X-ray images based on the pre-trained GoogLeNet model. The traditional GoogLeNet is adapted by modifying the final network layers and adopting 20-fold cross-validation to reduce the over-fitting problem.…”

Section: Related Workmentioning

confidence: 99%

Classification Framework for COVID-19 Diagnosis Based on Deep CNN Models

El-Shafai¹,

Algarni²,

Banby³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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Automated diagnosis based on medical images is a very promising trend in modern healthcare services. For the task of automated diagnosis, there should be flexibility to deal with an enormous amount of data represented in the form of medical images. In addition, efficient algorithms that could be adapted according to the nature of images should be used. The importance of automated medical diagnosis has been maximized with the evolution of COVID-19 pandemic. COVID-19 first appeared in China, Wuhan, and then it has exploded in the whole world with a very bad impact on our daily life. The third wave of COVID-19 in the third world is really a disaster in current days, especially with the emergence of the delta variant of COVID-19 that is widespread. Required inspections should be carried out to monitor the COVID-19 spread in daily life and allow primary diagnosis of suspected cases, and long-term clinical laboratory monitoring. Healthcare professionals or radiologists can exploit AI (Artificial Intelligence) tools to quickly and reliably identify the cases of COVID-19. This paper introduces a DCNN (Deep Convolutional Neural Network) framework for chest X-ray and CT image classification based on TL (Transfer Learning). The objective is to perform multi-class and binary classification of the images in order to determine pneumonia and COVID-19 case. The TL is feasible, when using a small dataset by transferring knowledge from natural image classification to medical image classification. Two types of TL are used. The first type is finetuning of the DenseNet121, Densenet169, DenseNet201, ResNet50, ResNet152, VGG16, and VGG19 models. The second type is deep tuning of the LeNet-5, AlexNet, Inception naïve v1, and VGG16 models. Extensive tests have been carried out on datasets of chest X-ray and CT images with different training/testing ratios of 80%:20%, 70%:30%, and 60%:40%. Experimental results on 9,270 chest X-ray ray and 2,762 chest CT images acquired from different institutions show that the TL is effective with an average accuracy of 98.49%.

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“…Storey et al [ 25 ] presented details of the EmotioNet challenge approach and results in [ 11 ]. This is the first task to put computer vision algorithms [ 26 ] to the test in terms of automatically analyzing a huge number of photos of facial expressions of emotion in the wild. The task was split into two sections.…”

Section: Related Workmentioning

confidence: 99%

Enhancement of Patient Facial Recognition through Deep Learning Algorithm: ConvNet

Onyema

Shukla

Dalal

et al. 2021

Journal of Healthcare Engineering

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The use of machine learning algorithms for facial expression recognition and patient monitoring is a growing area of research interest. In this study, we present a technique for facial expression recognition based on deep learning algorithm: convolutional neural network (ConvNet). Data were collected from the FER2013 dataset that contains samples of seven universal facial expressions for training. The results show that the presented technique improves facial expression recognition accuracy without encoding several layers of CNN that lead to a computationally costly model. This study proffers solutions to the issues of high computational cost due to the implementation of facial expression recognition by providing a model close to the accuracy of the state-of-the-art model. The study concludes that deep l\earning-enabled facial expression recognition techniques enhance accuracy, better facial recognition, and interpretation of facial expressions and features that promote efficiency and prediction in the health sector.

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Multiobjective Genetic Algorithm and Convolutional Neural Network Based COVID-19 Identification in Chest X-Ray Images

Cited by 136 publications

References 31 publications

Machine Learning Techniques for Human Age and Gender Identification Based on Teeth X-Ray Images

Machine Learning Techniques for Human Age and Gender Identification Based on Teeth X-Ray Images

Classification Framework for COVID-19 Diagnosis Based on Deep CNN Models

Enhancement of Patient Facial Recognition through Deep Learning Algorithm: ConvNet

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