A Recognition Method for Italian Alphabet Gestures Based on Convolutional Neural Network

Ji, Xiaoyu; Qi-ming, YU; Kong, Siran

doi:10.1007/978-3-030-26763-6_63

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…Nowadays, researchers are using CNN (Convolutional Neural Network), a class of deep learning neural networks for several applications [31] , [32] , [33] . CNNs have an input layer, an output layer, and hidden layers [34] . The hidden layers usually consist of convolutional layers, ReLU layers, pooling layers, and fully connected layers [35] , [36] .…”

Section: Introductionmentioning

confidence: 99%

Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network

Marques

Agarwal

Díez

2020

Applied Soft Computing

314

151

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COVID-19 infection was reported in December 2019 at Wuhan, China. This virus critically affects several countries such as the USA, Brazil, India and Italy. Numerous research units are working at their higher level of effort to develop novel methods to prevent and control this pandemic scenario. The main objective of this paper is to propose a medical decision support system using the implementation of a convolutional neural network (CNN). This CNN has been developed using EfficientNet architecture. To the best of the authors’ knowledge, there is no similar study that proposes an automated method for COVID-19 diagnosis using EfficientNet. Therefore, the main contribution is to present the results of a CNN developed using EfficientNet and 10-fold stratified cross-validation. This paper presents two main experiments. First, the binary classification results using images from COVID-19 patients and normal patients are shown. Second, the multi-class results using images from COVID-19, pneumonia and normal patients are discussed. The results show average accuracy values for binary and multi-class of 99.62% and 96.70%, respectively. On the one hand, the proposed CNN model using EfficientNet presents an average recall value of 99.63% and 96.69% concerning binary and multi-class, respectively. On the other hand, 99.64% is the average precision value reported by binary classification, and 97.54% is presented in multi-class. Finally, the average F1-score for multi-class is 97.11%, and 99.62% is presented for binary classification. In conclusion, the proposed architecture can provide an automated medical diagnostics system to support healthcare specialists for enhanced decision making during this pandemic scenario.

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Section: Introductionmentioning

confidence: 99%

Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network

Marques

Agarwal

Díez

2020

Applied Soft Computing

314

151

View full text Add to dashboard Cite

show abstract

“…In the development of studies related to character recognition, especially for the non-Latin alphabets known to possess a complex varied structure and shape, several scholars propose deep learning techniques with extraction and modeling feature [20]. This method adopts an advanced algorithm, which is supported by a reliable training data set and powerful computation proses, believed to capably provide a preferred solution in machine learning field, including image and pattern detection [21].…”

Section: Review Of Related Literaturementioning

confidence: 99%

The Javanese Letters Classifier with Mobile Client-Server Architecture and Convolution Neural Network Method

Harjoseputro

Handarkho

Adie

2019

Int. J. Interact. Mob. Technol.

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<p class="0abstract">the rapid development of mobile technologies allows platform devices to perform sophisticated tasks, including character recognition. These identification systems are notable techniques that required high computation cost, in order to achieve acceptable accuracy resulting from diversity in alphabet shape and method of writing, especially for the non-Latin alphabet, e.g., Javanese letter. In addition, numerous studies have attempted to address these issues by employing a Convolution Neural Network (CNN) due to its ability to provide high accuracy in character detection. However, the performance on mobile devices is possibly faced with problems resulting from the limitation of computation resource on the platform that also affect computation cost. This study, therefore, proposes a 2-tier architecture by placing the mobile app as a client that invokes a Javanese letters classifier service, which is based on CNN, and implemented in the web-server through the Application Program Interface (API). The results show that the letter classification was successfully implemented in a mobile platform, with an accuracy rate of 86.68%, utilizing training for 50 epochs, and an average time of 1935 ms.</p>

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An ensemble-based approach for automated medical diagnosis of malaria using EfficientNet

Marques

Ferreras

Díez

2022

Multimed Tools Appl

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Each year, more than 400,000 people die of malaria. Malaria is a mosquito-borne transmissible infection that affects humans and other animals. According to World Health Organization (WHO), 1.5 billion malaria cases and 7.6 million related deaths have been prevented from 2000 to 2019. Malaria is a disease that can be treated if early detected. We propose a support decision system for detecting malaria from microscopic peripheral blood cells images through convolutional neural networks (CNN). The proposed model is based on EfficientNetB0 architecture. The results are validated with 10-fold stratified cross-validation. This paper presents the classification findings using images from malaria patients and normal patients. The proposed approach is compared and outperforms the related work. Furthermore, the proposed ensemble method shows a recall value of 98.82%, a precision value of 97.74%, an F1-score of 98.28% and a ROC value of 99.76%. This work suggests that EfficientNet is a reliable architecture for automatic medical diagnostics of malaria. Supplementary Information The online version contains supplementary material available at 10.1007/s11042-022-12624-6.

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A Recognition Method for Italian Alphabet Gestures Based on Convolutional Neural Network

Cited by 6 publications

References 11 publications

Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network

Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network

The Javanese Letters Classifier with Mobile Client-Server Architecture and Convolution Neural Network Method

An ensemble-based approach for automated medical diagnosis of malaria using EfficientNet

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