A Deep Learning Approach for Recognizing Covid-19 from Chest X-ray using Modified CNN-BiLSTM with M-SVM

Sikder, Juel; Datta, Nippon; Tripura, Dhonita

doi:10.1109/icecet55527.2022.9872776

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…A callback function is also used at the time of model training. The callback function assists the classifier to save the weights when val loss is decreased [19], [20].…”

Section: ) Call Multi Support Vector Machinementioning

confidence: 99%

An Approach Based on Deep Learning for Recognizing Emotion, Gender and Age

Nippon

Juel

2022

Preprint

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At the same time, recognizing the emotions, gender and age of a person is a vital issue in real life. This research focuses on identifying the emotions along with age and gender of different ages male, female, common gender and children. In this study, a modified convolutional neural network (M-CNN) has been proposed for feature extraction where classification is followed by a Multi Support Vector Machine (M-SVM). Instead of detecting the face, a head-detecting technique is also used in the prediction part. To detect the head we proposed a system using the You Only Look Once Version 3 (YOLOV3) network. It helps to extract the feature from the whole head instead of the only face. Three datasets named AR-2022, FER-2022 and GR-2022 are introduced in this study. We also include the common gender in our datasets. In the case of age classification, we categorized the age datasets into 11 categories depending on the similarity of their features, gender into 3 categories and emotions into 7 categories. Our proposed model is compared with other existing models and current research, where the proposed model gives better accuracy and the accuracy of classification of emotions, age and gender are 97.47%, 98.43% and 96.65% respectively.

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“…A callback function is also used at the time of model training. The callback function assists the classifier to save the weights when val loss is decreased [19], [20].…”

Section: ) Call Multi Support Vector Machinementioning

confidence: 99%

An Approach Based on Deep Learning for Recognizing Emotion, Gender and Age

Nippon

Juel

2022

Preprint

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Emerging Trends and Challenges in Cybersecurity Data Science: A State-of-the-Art Review

Datta,

Mahmud,

Aziz

et al. 2024

2024 Parul International Conference on Engineering and Technology (PICET)

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Rapid and Precise Identification of COVID-19 through Segmentation and Classification of CT and X-ray Images

Saygılı

2023

Sakarya University Journal of Computer and Information Sciences

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The COVID-19 pandemic, caused by a novel coronavirus, has become a global epidemic. Although the reverse transcription-polymerase chain reaction (RT-PCR) test is the current gold standard for detecting the virus, its low reliability has led to the use of CT and X-ray imaging in diagnostics. As limited vaccine availability necessitates rapid and accurate detection, this study applies k-means and fuzzy c-means segmentation to CT and X-ray images to classify COVID-19 cases as either diseased or healthy for CT scans and diseased, healthy, or non-COVID pneumonia for X-rays. Our research employs four open-access, widely-used datasets and is conducted in four stages: preprocessing, segmentation, feature extraction, and classification. During feature extraction, we employ the Gray-Level Co-Occurrence Matrix (GLCM), Local Binary Pattern (LBP), and Histogram of Oriented Gradients (HOG). In the classification process, our approach involves utilizing k-Nearest Neighbor (kNN), Support Vector Machines (SVM), and Extreme Learning Machines (ELM) techniques. Our research achieved a sensitivity rate exceeding 99%, which is higher than the 60-70% sensitivity rate of PCR tests. As a result, our study can serve as a decision support system that can help medical professionals make rapid and precise diagnoses with a high level of sensitivity.

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A Deep Learning Approach for Recognizing Covid-19 from Chest X-ray using Modified CNN-BiLSTM with M-SVM

Cited by 3 publications

References 19 publications

An Approach Based on Deep Learning for Recognizing Emotion, Gender and Age

An Approach Based on Deep Learning for Recognizing Emotion, Gender and Age

Emerging Trends and Challenges in Cybersecurity Data Science: A State-of-the-Art Review

Rapid and Precise Identification of COVID-19 through Segmentation and Classification of CT and X-ray Images

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