Many high-secure applications are using biometrics for natural, user-friendly and quick authentication. Cryptography is meant to make sure the secrecy and authenticity of message and protecting the confidentiality of the cryptographic keys is one among the numerous problems to be dealt with. Researchers are examining suggests that to utilize biometric options of the user to get sturdy and repeatable cryptographic keys rather than a memorable password. This may be efficiently solved by the combination of biometrics with cryptography. This paper presents ways for generating the strong bio-crypt key based mostly on fingerprint. Fingerprint biometric modality is predominantly thought of due to its two vital characteristics uniqueness and permanence that's ability to stay unchanged over the lifetime.
Objectives:
Radiological lung changes in COVID-19 infections present a noteworthy avenue to develop chest X-ray (CXR) -based testing models to support existing rapid detection techniques. The purpose of this study is to evaluate the accuracy of artificial intelligence (AI) -based screening model employing deep convolutional neural network for lung involvement.
Material and Methods:
An AI-based screening model was developed with state-of-the-art neural networks using Indian data sets from COVID-19 positive patients by authors of CAIR, DRDO, in collaboration with the other authors. Our dataset was comprised of 1324 COVID-19, 1108 Normal, and 1344 Pneumonia CXR images. Transfer learning was carried out on Indian dataset using popular deep neural networks, which includes DenseNet, ResNet50, and ResNet18 network architectures to classify CXRs into three categories. The model was retrospectively used to test CXRs from reverse transcriptase-polymerase chain reaction (RT-PCR) proven COVID-19 patients to test positive predictive value and accuracy.
Results:
A total of 460 RT-PCR positive hospitalized patients CXRs in various stages of disease involvement were retrospectively analyzed. There were 248 males (53.92%) and 212 females (46.08%) in the cohort, with a mean age of 50.1 years (range 12–89 years). The commonly observed alterations included lung consolidations, ground-glass opacities, and reticular–nodular opacities. Bilateral involvement was more common compared to unilateral involvement. Of the 460 CXRs analyzed, the model reported 445 CXRs as COVID -19 with an accuracy of 96.73%.
Conclusion:
Our model, based on a two-level classification decision fusion and output information computation, makes it a robust, accurate and reproducible tool. Based on the initial promising results, our application can be used for mass screening.
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