Unmanned Air Vehicles (UAVs) are becoming popular in real-world scenarios due to current advances in sensor technology and hardware platform development. The applications of UAVs in the medical field are broad and may be shared worldwide. With the recent outbreak of COVID-19, fast diagnostic testing has become one of the challenges due to the lack of test kits. UAVs can help in tackling the COVID-19 by delivering medication to the hospital on time. In this paper, to detect the number of COVID-19 cases in a hospital, we propose a deep convolution neural architecture using transfer learning, classifying the patient into three categories as COVID-19 (positive) and normal (negative), and pneumonia based on given X-ray images. The proposed deep-learning architecture is compared with state-of-the-art models. The results show that the proposed model provides an accuracy of 94.92%. Further to offer time-bounded services to COVID-19 patients, we have proposed a scheme for delivering emergency kits to the hospitals in need using an optimal path planning approach for UAVs in the network.
Keyphrases
- sars cov
- coronavirus disease
- healthcare
- deep learning
- emergency department
- primary care
- public health
- convolutional neural network
- respiratory syndrome coronavirus
- mental health
- artificial intelligence
- adverse drug
- high resolution
- optical coherence tomography
- intensive care unit
- machine learning
- magnetic resonance imaging
- case report
- network analysis
- sensitive detection
- contrast enhanced
- acute respiratory distress syndrome