A comparative analysis of chronic obstructive pulmonary disease using machine learning, and deep learning

Chronic obstructive pulmonary disease (COPD) is a general clinical issue in numerous countries considered the fifth reason for inability and the third reason for mortality on a global scale within 2021. From recent reviews, a deep convolutional neural network (CNN) is used in the primary analysis of the deadly COPD, which uses the computed tomography (CT) images procured from the deep learning tools. Detection and analysis of COPD using several image processing techniques, deep learning models, and machine learning models are notable contributions to this review. This research aims to cover the detailed findings on pulmonary diseases or lung diseases, their causes, and symptoms, which will help treat infections with high performance and a swift response. The articles selected have more than 80% accuracy and are tabulated and analyzed for sensitivity, specificity, and area under the curve (AUC) using different methodologies. This research focuses on the various tools and techniques used in COPD analysis and eventually provides an overview of COPD with coronavirus disease 2019 (COVID-19) symptoms.

Design and development of DrawBot using image processing

Extracting text from an image and reproducing them can often be a laborious task. We took it upon ourselves to solve the problem. Our work is aimed at designing a robot which can perceive an image shown to it and reproduce it on any given area as directed. It does so by first taking an input image and performing image processing operations on the image to improve its readability. Then the text in the image is recognized by the program. Points for each letter are taken, then inverse kinematics is done for each point with MATLAB/Simulink and the angles in which the servo motors should be moved are found out and stored in the Arduino. Using these angles, the control algorithm is generated in the Arduino and the letters are drawn