The COVID-19 pandemic has caused a worldwide catastrophe and widespread devastation that reeled a... more The COVID-19 pandemic has caused a worldwide catastrophe and widespread devastation that reeled almost all countries. The pandemic has mounted pressure on the existing healthcare system and caused panic and desperation. The gold testing standard for COVID-19 detection, reverse transcription-polymerase chain reaction (RT-PCR), has shown its limitations with 70% accuracy, contributing to the incorrect diagnosis that exaggerated the complexities and increased the fatalities. The new variations further pose unseen challenges in terms of their diagnosis and subsequent treatment. The COVID-19 virus heavily impacts the lungs and fills the air sacs with fluid causing pneumonia. Thus, chest X-ray inspection is a viable option if the inspection detects COVID-19-induced pneumonia, hence confirming the exposure of COVID-19. Artificial intelligence and machine learning techniques are capable of examining chest X-rays in order to detect patterns that can confirm the presence of COVID-19-induced p...
As a result of the Covid-19 pandemic, the field of Medical Sciences has been challenged with new ... more As a result of the Covid-19 pandemic, the field of Medical Sciences has been challenged with new challenges and benchmarks for development. Front line workers are overcoming the Covid-19 challenge with four steps: Screening and Diagnosis, Contact Tracing, Drug and Vaccine Development, and Prediction & Forecasting. Following the above segments carefully can save millions of lives. Artificial Intelligence has proven invaluable in predicting critical factors in many fields. With the ability of AI to process huge databases and conclude with high precision, we are motivated to use AI to screen and diagnose the Covid-19 pandemic. This paper examines the strategic use of Transfer Learning for screening and diagnosis of Covid-19 Patients. The Xception model is used to categorize Covid-19 infected patients. Our proposed Xception model has achieved better Accuracy, Sensitivity and Specificity as compared with state-of-the-art models.
2021 IEEE International Conference on Mobile Networks and Wireless Communications (ICMNWC), 2021
Machine learning approaches typically need large volumes of training data and often demand costly... more Machine learning approaches typically need large volumes of training data and often demand costly manual etiquette to achieve their maximum ability. The value of images produced solely by visual means is on the increase. The color histogram is commonly used for the image classification challenge as a significant color characteristic that indicates the material. Since synthetic representations are an essential means through which visual knowledge is collected and presented. It is increasingly critical that these images are accurately classified in sub-categories – such as symbols, diagrams, figures and tables, logos, etc. The proposed research work aims at classifying synthetic images into sub-categories. The essential features of images shall be extensively analyzed and processed. When the web begins, photographs are used to convey content and beautify, shape, and align. In particular, the work is directed to image recognition techniques to identify pictures that are of great interest. This work tries to classify synthetic images from different images using a machine learning approach.
The COVID-19 pandemic has caused a worldwide catastrophe and widespread devastation that reeled a... more The COVID-19 pandemic has caused a worldwide catastrophe and widespread devastation that reeled almost all countries. The pandemic has mounted pressure on the existing healthcare system and caused panic and desperation. The gold testing standard for COVID-19 detection, reverse transcription-polymerase chain reaction (RT-PCR), has shown its limitations with 70% accuracy, contributing to the incorrect diagnosis that exaggerated the complexities and increased the fatalities. The new variations further pose unseen challenges in terms of their diagnosis and subsequent treatment. The COVID-19 virus heavily impacts the lungs and fills the air sacs with fluid causing pneumonia. Thus, chest X-ray inspection is a viable option if the inspection detects COVID-19-induced pneumonia, hence confirming the exposure of COVID-19. Artificial intelligence and machine learning techniques are capable of examining chest X-rays in order to detect patterns that can confirm the presence of COVID-19-induced p...
As a result of the Covid-19 pandemic, the field of Medical Sciences has been challenged with new ... more As a result of the Covid-19 pandemic, the field of Medical Sciences has been challenged with new challenges and benchmarks for development. Front line workers are overcoming the Covid-19 challenge with four steps: Screening and Diagnosis, Contact Tracing, Drug and Vaccine Development, and Prediction & Forecasting. Following the above segments carefully can save millions of lives. Artificial Intelligence has proven invaluable in predicting critical factors in many fields. With the ability of AI to process huge databases and conclude with high precision, we are motivated to use AI to screen and diagnose the Covid-19 pandemic. This paper examines the strategic use of Transfer Learning for screening and diagnosis of Covid-19 Patients. The Xception model is used to categorize Covid-19 infected patients. Our proposed Xception model has achieved better Accuracy, Sensitivity and Specificity as compared with state-of-the-art models.
2021 IEEE International Conference on Mobile Networks and Wireless Communications (ICMNWC), 2021
Machine learning approaches typically need large volumes of training data and often demand costly... more Machine learning approaches typically need large volumes of training data and often demand costly manual etiquette to achieve their maximum ability. The value of images produced solely by visual means is on the increase. The color histogram is commonly used for the image classification challenge as a significant color characteristic that indicates the material. Since synthetic representations are an essential means through which visual knowledge is collected and presented. It is increasingly critical that these images are accurately classified in sub-categories – such as symbols, diagrams, figures and tables, logos, etc. The proposed research work aims at classifying synthetic images into sub-categories. The essential features of images shall be extensively analyzed and processed. When the web begins, photographs are used to convey content and beautify, shape, and align. In particular, the work is directed to image recognition techniques to identify pictures that are of great interest. This work tries to classify synthetic images from different images using a machine learning approach.
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Papers by dr neeraj sahu