DeepCOVID-XR, an artificial intelligence algorithm for detecting coronavirus disease 2019 on chest radiographs, demonstrated performance similar to that of the consensus of experienced thoracic rad...

https://pubs.rsna.org/doi/pdf/10.1148/radiol.2020203511

DeepCOVID-XR: An Artificial Intelligence Algorithm to Detect COVID-19 on Chest Radiographs Trained and Tested on a Large U.S. Clinical Data Set.

Customized VGG19 Architecture for Pneumonia Detection in Chest X-Rays

Part defects and irregularities that influence the part quality is an especially large problem in additive manufacturing (AM) processes such as selective laser sintering (SLS). Destructive and non-destructive testing procedures are currently mostly used for quality control and defect detection of AM parts after production. In this context, machine learning (ML) algorithms are increasingly being used to enable computer-aided defect detection through automatic classification of manufacturing data. Convolutional neural networks (CNN) based on ML methods are widely used for this task. In this paper, complex transfer learning (TL) methods are presented, which enable the automatic classification of powder bed defects in the SLS process using very small datasets. The proposed methods use the VGG16 and the Xception CNN model with pretrained weights from the ImageNet dataset as initialization and an adapted classifier to classify good and defective image data recorded during part manufacturing. Known performance metrics were determined to evaluate and compare the performance of the models. The VGG16 model architecture achieved the best results for Accuracy (0.958), Precision (0.939), Recall (0.980), F1-Score (0.959) and AUC value (0.982). These results show the effectiveness of defect detection based on CNN and can offer an alternative method for non-destructive quality assurance and manufacturing documentation for additively manufactured parts.

A machine learning method for defect detection and visualization in selective laser sintering based on convolutional neural networks

The spread of COVID-19 has been taken on pandemic magnitudes and has already spread over 200 countries in a few months. In this time of emergency of COVID-19, especially when there is still a need to follow the precautions and developed vaccines are not available to all the developing countries in the first phase of vaccine distribution, the virus is spreading rapidly through direct and indirect contacts. The World Health Organization (WHO) provides the standard recommendations on preventing the spread of COVID-19 and the importance of face masks for protection from the virus. The excessive use of manual disinfection systems has also become a source of infection. That is why this research aims to design and develop a low-cost, rapid, scalable, and effective virus spread control and screening system to minimize the chances and risk of spread of COVID-19. We proposed an IoT-based Smart Screening and Disinfection Walkthrough Gate (SSDWG) for all public places entrance. The SSDWG is designed to do rapid screening, including temperature measuring using a contact-free sensor and storing the record of the suspected individual for further control and monitoring. Our proposed IoT-based screening system also implemented real-time deep learning models for face mask detection and classification. This module classified individuals who wear the face mask properly, improperly, and without a face mask using VGG-16, MobileNetV2, Inception v3, ResNet-50, and CNN using a transfer learning approach. We achieved the highest accuracy of 99.81% while using VGG-16 and the second highest accuracy of 99.6% using MobileNetV2 in the mask detection and classification module. We also implemented classification to classify the types of face masks worn by the individuals, either N-95 or surgical masks. We also compared the results of our proposed system with state-of-the-art methods, and we highly suggested that our system could be used to prevent the spread of local transmission and reduce the chances of human carriers of COVID-19.

https://www.mdpi.com/2076-3417/11/8/3495/pdf

IoT and Deep Learning Based Approach for Rapid Screening and Face Mask Detection for Infection Spread Control of COVID-19

Transfer learning for medical images analyses: A survey

Pneumonia causes the death of around 700,000 children every year and affects 7% of the global population Chest X-rays are primarily used for the diagnosis of this disease However, even for a trained radiologist, it is a challenging task to examine chest X-rays There is a need to improve the diagnosis accuracy In this work, an efficient model for the detection of pneumonia trained on digital chest X-ray images is proposed, which could aid the radiologists in their decision making process A novel approach based on a weighted classifier is introduced, which combines the weighted predictions from the state-of-the-art deep learning models such as ResNet18, Xception, InceptionV3, DenseNet121, and MobileNetV3 in an optimal way This approach is a supervised learning approach in which the network predicts the result based on the quality of the dataset used Transfer learning is used to fine-tune the deep learning models to obtain higher training and validation accuracy Partial data augmentation techniques are employed to increase the training dataset in a balanced way The proposed weighted classifier is able to outperform all the individual models Finally, the model is evaluated, not only in terms of test accuracy, but also in the AUC score The final proposed weighted classifier model is able to achieve a test accuracy of 9843% and an AUC score of 9976 on the unseen data from the Guangzhou Women and Children’s Medical Center pneumonia dataset Hence, the proposed model can be used for a quick diagnosis of pneumonia and can aid the radiologists in the diagnosis process

Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning.

Pneumonia is the leading cause of death worldwide for children under 5 years of age. For pneumonia diagnosis, chest X-rays are examined by trained radiologists. However, this process is tedious and...

Pneumonia detection in chest X-ray images using compound scaled deep learning model

Contactless authentication has become a trending topic these days, especially in this Corona pandemic situation, where contactless access systems are precautionary steps. The two most important contactless authentication systems right now are facial recognition and gait recognition. With having a possibility of spook attacks on facial recognition, gait patterns have gained popularity especially in the high-secured zones. Gait biometrics is considered as the next generation of biometrics which can enable identification of subjects even through a certain distance, avoiding any surface-contact. However, complex challenges gait patterns are mainly the view angles and distances for environmental setup and crucial gait features for algorithm setup. Hence, the authors are extending their previous work which is identifying ‘specific targets’ based on their gait patterns in Defence applications. We combined facial recognition designed with our anti-spoofing algorithm and gait patterns which gave on an average 92% accuracy irrespective of their background, motion, and angle.

Satyarth Katiyar

Papers

Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning.

Pneumonia detection in chest X-ray images using compound scaled deep learning model

AccessNet: A Three Layered Visual Based Access Authentication System for Restricted Zones