Biomedical Image Classification Using Convolutional Neural Networks

Recent research trends in the field image processing have focussed on challenges and few techniques for processing and classification tasks related to it. Image classification aims at classifying images based on several predefined categories. Several research works have been carried out to overcome shortcomings in image classification, nevertheless the output was restricted to the elementary low-level picture. Several deep neural network techniques are employed for image classification such as Convolutional Neural Network, Machine Learning Algorithms like Random Forest, SVM, etc. In this paper, we aim at designing a COVID-19 detection using the CNN model with support of Open-Source software such as Keras, Python, Google Colab, Google Drive, Kaggle, and Visual Studio for aggregate, design, create, train, visualize, and analyze bulk load of data on the cloud after programing a Deep neural network without a need for high-end processing hardware. We have made use of weights to test and analyse the accuracy, visualize and predict the condition of a lung using chest X-Rays at certain accuracy. This will help in identifying the problems of the patients at a faster rate, thus giving an appropriate treatment at an early stage itself to saving one life. © 2023 IEEE.

IMPLEMENTATION OF OBJECT DETECTION WITH YOU ONLY LOOK ONCE ALGORITHM IN LIMITED FACE-TO-FACE TIMES IN PANDEMIC

Covid-19 has hit many countries in the world, including Indonesia. The rapid and deadly spread of Covid-19 reached Indonesia in early 2020. This pandemic of course had a detrimental impact on the Indonesian people in terms of health, economy, education and others. The Indonesian government certainly does not remain silent, the government is aggressively making efforts to break the Covid-19 chain in various ways, one of the efforts made is to continuously inform about the health protocols recommended by the government to prevent transmission. The Indonesian Ministry of Health, Efforts that can be made in the prevention phase by each individual include: Wearing a mask, Wearing gloves, Using hand sanitizer/disinfectant, Washing hands with soap, Avoiding touching the face, Avoid shaking hands, Avoid gatherings or long queues, Avoid touching objects/object surfaces in public areas, Avoid taking public transportation, Maintain a distance of at least two meters from other people when outside the house, and If you show symptoms of illness, immediately notify the people around. Even though they have been informed about the prevention of Covid-19, the public tends to be negligent in implementing health protocols, one of which is the application of Social Distancing. Therefore, this study will create a distance detector using the YOLOv3 algorithm as one of the detection objects for the implementation of community activity restrictions. © 2022, Intellectual Research and Development Education Foundation (YRPI). All rights reserved.