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.

Cognitive Computing in diagnosis of Covid-19 from CT scan and radiograph images: A comparative study

The dreadful coronavirus has not only shattered the lives of millions of people, but it has also placed enormous strain on the whole healthcare system. In order to isolate positive cases and stop the disease from spreading, early detection of COVID-19 is crucial. Currently, a laboratory test (RT-PCR) on samples collected from the throat and nose is required for the official diagnosis of COVID-19. Specialized tools are needed for the RT-PCR test, which takes at least 24 hours to complete. It may often provide more false negative and false positive results than expected. Therefore, using X-ray and CT scan images of the individual's lung, COVID-19 screening can be used to support the conventional RTPCR methods for an accurate clinical diagnosis. The importance of chest imaging in the emergence of this lung illness has been recognized. Images from the computed tomography (CT) scan and chest X-ray (CXR) can be used to quickly and accurately diagnose COVID-19. However, CT scan pictures have their own drawbacks. In order to assess the effectiveness of chest imaging approaches and demonstrates that CXR as an input may compete with CT scan pictures in the diagnosis of COVID-19 infection using various CNN based models, this article thoroughly covers modern deep learning techniques (CNN). For CXR and CT scan pictures, we have evaluated with ResNet, MobileNet, VGG 16, and EfficientNet. Both chest X-ray (3604 Images) and CT scans (3227 images) from publicly accessible databases have been evaluated, and the experimental outcomes are also contrasted. © 2022 IEEE.

Real-time multiple face mask and fever detection using YOLOv3 and TensorFlow lite platforms

COVID-19 has caused disruptions to many aspects of everyday life. To reduce the impact of this pandemic, its spreading must be controlled via face mask wearing. Manually mask-checking for everybody is embarrassing and uncontrollable. Hence, the proposed technique is used to help for automatic mask-checking based on deep learning platforms with real-time surveillance live infra-red (IR) camera. In this paper, two recent object detection platforms, named, you only look once version 3 (YOLOv3) and TensorFlow lite are adopted to accomplish this task. The two models are trained with a dataset consisting of images of persons with/without masks. This work is simulated with Google Colab then tested in real-time on an embedded device mated with fast GPU called Raspberry Pi 4 model B, 8 GB RAM. A comparison is made between the two models to verify their performance in relation to their precision rate and processing time. The work of this paper is also succeeded to realize multiple face masks real-time detection up to 10 facemasks in a single scene with high inference speed. Temperature is also measured using IR touchless sensor for each person with sound alarming to alert fever. The presented detector is cheap, light, small, and fast, with 99% accuracy rate during training and testing. © 2023, Institute of Advanced Engineering and Science. All rights reserved.

Developing Computing Competencies Without Restrictions

The information society represents a great revolution. Computing programming is a relevant competence nowadays for everybody, regardless of educational background. However, traditional programming languages consider syntax barriers that complicate their adoption and usefulness for beginners. Python is an exception for its open-source, cross-platform nature and syntax simplicity, which facilitate the development of algorithmic thinking and dissemination of programming solutions. Several Python extensions support modern functionalities such as web development, videogame, and machine learning, making it one of the most used programming languages. Google Colab or Colaboratory facilitates the online learning and development of Python solutions. This article presents positive academic experiences of Chilean students of majors from two Chilean universities, a traditional university in the north and a private university in the middle of Chile, using Google Colab to develop programming competencies remotely for the Covid pandemic. We highlight the promising results obtained for basic programming and operating system programming subjects, which motivate us to use Python and Google Colab widely, not only in university contexts. We expect to continue developing programming competencies using Google Colab and Python. The main limitation encountered in this experience is the internet connection requirements for online education. However, it does not represent an issue for education in developing and developed countries. Google Colab permits the development of highly demanded competencies worldwide at home, only with internet access and a web browse, an excellent motivation for learning for all students regardless of age and academic level.

Teaching genomics to life science undergraduates using cloud computing platforms with open datasets.

The final year of a biochemistry degree is usually a time to experience research. However, laboratory-based research projects were not possible during COVID-19. Instead, we used open datasets to provide computational research projects in metagenomics to biochemistry undergraduates (80 students with limited computing experience). We aimed to give the students a chance to explore any dataset, rather than use a small number of artificial datasets (~60 published datasets were used). To achieve this, we utilized Google Colaboratory (Colab), a virtual computing environment. Colab was used as a framework to retrieve raw sequencing data (analyzed with QIIME2) and generate visualizations. Setting up the environment requires no prior experience; all students have the same drive structure and notebooks can be shared (for synchronous sessions). We also used the platform to combine multiple datasets, perform a meta-analysis, and allowed the students to analyze large datasets with 1000s of subjects and factors. Projects that required increased computational resources were integrated with Google Cloud Compute. In future, all research projects can include some aspects of reanalyzing public data, providing students with data science experience. Colab is also an excellent environment in which to develop data skills in multiple languages (e.g., Perl, Python, Julia).

Development Of A Deep Learning Model To Classify X-Ray Of Covid-19, Normal And Pneumonia-Affected Patients

Pneumonia is commonly seen in several diseases, including Covid-19 that has put countries under lockdown today [1]. Other than antigen rapid test kit (RTK) and reverse transcription-polymerase chain reaction (RT-PCR), an alternative method to detect COVID-19 is through the examination of patients' chest radiography (CXR). However, the results of manual inspections may be false and the misdiagnosis could lead to fatal consequences such as delayed treatment and death. The manual inspection can be inconsistent, inaccurate and may differ from different individuals due to different perspectives. Often, Covid-19 Xrays are misinterpreted as bacterial pneumonia. With the advancement of technology, this issue can be overcome by developing a Convolutional Neural Network (CNN) model to categorize X-ray of normal, pneumonia-affected and COVID-19 patients via deep learning. In this work, various CNN models (ResNet-50, ResNet-101, Vgg-16, Vgg-19 and SqueezeNet) were trained with the public databases that contain a combination of 1345 viral pneumonia, 1200 COVID-19 in addition to 1341 regular CXR images. The transfer learning method was employed, aided by image augmentation for training and validation of ResNet-50, ResNet-101, Vgg-16 and Vgg-19 architectures. Meanwhile, SqueezeNet was trained from scratch to investigate the importance of transfer learning to the model. The highest training accuracy achieved in this study was 97.38% by the VGG-16 model using a learning rate of 0.01 whereas the highest weighted average accuracy achieved was 94% by the VGG-16 model using a learning rate of 0.01 and the VGG-19 model using a learning rate of 0.001. The reliability and high accuracy of the CNN model would open a new avenue for the diagnosis of Covid-19. © 2021 IEEE

Meter Reading using OCR for Comfort and Cost Optimization

Meter reading and billing are time-consuming activities for power, water and gas providing boards. The existing billing system relies on a manual method of taking meter readings, updating the reading in the server and finally generating the bill amount. In this project, the user simply needs to use an Android application to capture and upload the picture of the meter after performing OCR operation. The processing on the image is performed on the server side using Google Colab and Python. The meter reading obtained from OCR processing is sent to the firebase, which is further pushed to the Android application. And finally the Android application displays the meter reading and the bill amount generated. Our project ensures the safety (from communicable diseases like COVID-19) of both the board staff and the customer as they don't come in contact with each other. This project also helps in cutting down on their expenditure by reducing manpower and travel costs. © 2021 IEEE.