A Convolutional Neural Network Based on Transfer Learning for Medical Waste Classification During Pandemic Covid-19

Globally, the pandemic of the coronavirus disease (COVID-19) is spreading quickly. Inadequate handling of contaminated garbage and waste management can unintentionally transmit the virus within the company. the complete spectrum from waste generation to treatment must be re-evaluated to scale back the socio-economic and environmental impacts of waste and help achieve a sustainable society. In the area of computer vision, deep learning is beginning to demonstrate high efficiency and minimal complexity. However, the problem now is the performance of the various CNN architectures with transfer learning compared to the classification of medical waste images. Using data augmentation, and preprocessing before performing the two-stage classification of medical waste classification. The research obtained an accuracy of 99.40%, a sensitivity of 98.18%, and a specificity of 100% without overfitting. © 2022 IEEE.

Classification of Covid Patients Based on Detection of Lung X-Rays Using Local Binary Pattern Method

High number of deaths due to Covid-19 outbreak affect people in various ways including their economic and psychological side. Previous studies were carried out in analyzing various symptoms in COVID-19 patients. Patients in severe conditions are usually found with a white spot in their lungs. Therefore chest x-ray is one of the necessary medical assessment to examine the patients. This study focus on determining whether a patient suffered from COVID-19 by analyzing their chest X-rays photos. A total of 864 X-rays photos were used as a dataset. Earlier steps in processing the dataset included removing the noise, equalizing the size and increasing the accuracy value. The Local Binary Pattern (LBP) method was used to extract the dataset feature. The performance analysis result was a precision value of 78.5%, recall of 78%, and f-measure of 79%. © 2021 IEEE.