ABSTRACT
Context: Automated detection of COVID-19 in real time can greatly help clinicians to handle increasing number of cases for preliminary screening. Deep CNN models trained with sufficiently large datasets may become best candidates to meet the purpose. Aims: This study aims for automated detection and classification of COVID-19 and viral pneumonia diseases by applying deep CNN model using chest X-ray images. The proposed model performs multiclass classification to meet the purpose. Settings and Design: The proposed model is built on top of VGG16 architecture with pretrained ImageNet weights. The model was fine-tuned using additional custom layers to deliver better performance specific to the target. Subjects and Methods: A total of 15,153 samples are used in this work. These samples include chest X-ray images of COVID-19, viral pneumonia, and normal cases. The entire dataset was split into train and test sets, with a ratio of 80:20 before training the model. To enhance important image features, image preprocessing and augmentation were applied before feeding the image batches to the model. Statistical Analysis Used: Performance of the model is evaluated through accuracy, precision, recall, and F1 score performance metrics. The results produced by the model are also compared with other recent leading studies. Results: The proposed model has achieved a classification accuracy of 98% with 98% precision, 96% recall, and 97% F1 score on the test dataset for multiclass classification. The area under receiver operating characteristic curve score was 0.99 for all three cases of multiclass classification. Conclusions: The proposed classification model may be highly useful for the preliminary diagnosis of COVID-19 and viral pneumonia cases, especially during heavy workloads and large quantities.
ABSTRACT
The knee is the biggest and complicated lower extremity joint that supports mobility and the entire weight of the human body and lies between the hip joint and ankle joint. Osteoarthritis (OA) is the most common joint disease in the knee among various musculoskeletal disorders globally, with an age-associated increase in incidence and prevalence. Health monitoring of the knee joints in daily life, and early OA diagnosis is challenging and draws attention to the various methods of diagnosis for this irreversible disease. In this review, electronic databases have been searched from inception for a detailed study about knee OA and its management. It focuses on various sensor technologies and different semi-invasive and non-invasive diagnosis methods with their limitations. In the last decade, various researchers have engrossed their attention to the potential of piezoelectric-based acoustic sensors to fabricate a wearable device for OA and its management. A sensor-based wearable device using vibroarthrography as a tool can be an appropriate solution for early-stage disease detection. We firmly believe that wearable technology for the detection of OA in daily life activities will play a significant role in managing this disease and help to reduce the chances of total knee replacements.
