Covid-19 Detection and Classification using Transfer learning with XGboost

The 2019 new corona virus (COVID-19), with a genesis phase in China, has dispersed apace amid individuals subsisting in distinct nations and is rising toward about twelve lakh cases in the balance as per the intuition of the European center for Health Security and Communicable diseases and ECDC. There is a foreordained figure of COVID-19 trial caskets attainable in medical centers because of the escalating cases in day-to-day life. In this way, it is important to execute a programmed location framework as a snappy elective conclusion alternative to forestall COVID-19 transmitting between peoples. In this examination, three disparate Convolutional neural system- based models (XGBOOST/LIGHTGBM, Inception-ResNetV2 and InceptionV3) have been put forward for the whereabouts of coronavirus and pneumonia contaminated convalescent by harnessing thoracic radiographic screening. Receiver Operating Characteristics (ROC) investigations and disordered networks by those tripartite models are bestowed and deteriorated by exploiting 5-superimpose traverse accredit. Contemplating the demonstration outcome obtained, it is perceived that the pre- prepared XGBOOST/LIGHTGBM model accouters the most upraised characterization execution with 98.6% exactness amongst the other two propounded models (96% correctness for InceptionV3 and 85% exactness for Inception-ResNetV2). © 2023 IEEE.

Face Mask Detection using CNN

Face recognition is an important feature of computer vision. It is used to detect a face and recognize a person and verify the person correctly. Face recognition technology plays an essential role in our everyday lives like in passport checking, smart door, access control, voter verification, criminal investigation, and system to secure public places such as parks, airports, bus stations, and railway stations, etc and many other purposes. While going through the pandemic and the post pandemic situations wearing a mask are compulsory for everyone in order to prevent the transmission of corona virus. This resulted in ineffectiveness of the existing conventional face recognition systems. Hence it is required to improvise the existing systems to get the desired results to detect the masked face at the earliest. This system works in three processes that are image pre-processing, image detection, and image classification. The main aim is to identify that whether a person’s face is covered with mask or not as per the CCTV camera surveillance or a webcam recording. It keeps on checking if a person is wearing mask or not. For classification, feature extraction and detection of the masked faces, Convolutional Neural Network (CNN) and Caffe models are used. These help in easy detection of masked faces with higher accuracy in a very less time and with high security. © 2021 IEEE.

COVID 19 prediction using convolutional neural network

Corona virus disease (COVID19) is a hastily spreadable disease that is wreaking havoc on medicalcare systems all over the world. Due to the drawbacks of rear transcription-polymerase chain reaction (RT-PCR)based tests for COVID19 detection, a count of recent modules have proposed radiology imaging-based ideas.CT imaging is critical for detecting COVID-19-related lung manifestations, and segmenting infected parts from CT scans is critical for quantitative disease progression estimation in exact and correct diagnosis and follow throughassessment.In this research, we use a Convolution Neural Network to predict COVID19 disease in chest CT scan images. It's an advanced lung infection detection system based on computed tomography (CT) images that has a lot of potential as a complement to the COVID-19 treatment strategy.