Related Papers
Advances in Intelligent Systems and Computing
Soft Computing: Theories and Applications
kanad ray
IEEE Access
Applying Different Machine Learning Techniques for Prediction of COVID-19 Severity
Abeer Korany
Healthcare
Empirical Study on Classifiers for Earlier Prediction of COVID-19 Infection Cure and Death Rate in the Indian States
2022 •
Shakeel Ahmed, PhD, Pratiyush Guleria
Empirical Study on Classifiers for Earlier Prediction of COVID-19 Infection Cure and Death Rate in the Indian States
Journal of Sensors
Fire-Net: A Deep Learning Framework for Active Forest Fire Detection
Vahideh Saeidi
Forest conservation is crucial for the maintenance of a healthy and thriving ecosystem. The field of remote sensing (RS) has been integral with the wide adoption of computer vision and sensor technologies for forest land observation. One critical area of interest is the detection of active forest fires. A forest fire, which occurs naturally or manually induced, can quickly sweep through vast amounts of land, leaving behind unfathomable damage and loss of lives. Automatic detection of active forest fires (and burning biomass) is hence an important area to pursue to avoid unwanted catastrophes. Early fire detection can also be useful for decision makers to plan mitigation strategies as well as extinguishing efforts. In this paper, we present a deep learning framework called Fire-Net, that is trained on Landsat-8 imagery for the detection of active fires and burning biomass. Specifically, we fuse the optical (Red, Green, and Blue) and thermal modalities from the images for a more effec...
Multimedia Tools and Applications
Classification of COVID-19 from chest x-ray images using deep features and correlation coefficient
Ridhi Arora
Acute Lymphoblastic Leukemia Detection from Microscopic Images Using Weighted Ensemble of Convolutional Neural Networks
Chayan Mondal
Although automated Acute Lymphoblastic Leukemia (ALL) detection is essential, it is challenging due to the morphological correlation between malignant and normal cells. The traditional ALL classification strategy is arduous, time-consuming, often suffers inter-observer variations, and necessitates experienced pathologists. This article has automated the ALL detection task, employing deep Convolutional Neural Networks (CNNs). We explore the weighted ensemble of deep CNNs to recommend a better ALL cell classifier. The weights are estimated from ensemble candidates' corresponding metrics, such as accuracy, F1-score, AUC, and kappa values. Various data augmentations and pre-processing are incorporated for achieving a better generalization of the network. We train and evaluate the proposed model utilizing the publicly available C-NMC-2019 ALL dataset. Our proposed weighted ensemble model has outputted a weighted F1-score of 88.6%, a balanced accuracy of 86.2%, and an AUC of 0.941 in ...
Computers, Materials & Continua
Malaria Blood Smear Classification Using Deep Learning and Best Features Selection
Talha Imran
Master s Final Project Machine Learning applied to COVID-19
2020 •
Serena Alderisi
This work is focused on the impact of machine learning, on the COVID-19 pandemic. Machine learning has proven to be invaluable in predicting risks in many spheres and since the spread of the virus started, its application is helping us against the viral pandemic. Like never before, people all around the world are collecting and sharing what they learn about the virus. Hundreds of research teams are combining their efforts to collect data and develop solutions every day. Starting from this, the main goals of this work are: to shine a light on their work; going deep into how the application of machine learning techniques on different fields affected by the pandemic is helping us in the fight against the coronavirus; to identify strengths and weaknesses of machine learning techniques and the challenges for further progress in medical machine learning systems. This final master thesis report addresses recent studies that apply machine learning on multiple angles: screening and diagnosis...
Prognosis Patients with COVID-19 using Deep Learning
2021 •
Etna Flores
Background: Prognostics study the prediction of an event before it happens, to enable critical decision making to be more efficient. The prognostics are very useful for front line physicians to predict how a disease may affect a patient and react accordingly to save the patients’ lives. The coronavirus (COVID-19) is novel and not enough knowledge about the virus’ behaviour and Key performance indicators (KPIs) to assess the mortality risk prediction. However, using a lot of complex and expensive medical biomarkers could be impossible for many low-budget hospitals. This motivates the development of a prediction model that not only maximizes performance but does so using the least number of biomarkers possible. Methods: For the mortality risk prediction, this research work proposes aCOVID-19 mortality risk calculator based on a Deep Learning (DL) model, and based on a data set provided by the HM Hospitals from Madrid, Spain. A pre-processing strategy for unbalanced classes and feature...
BioMedical Engineering OnLine
Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection
Haifang Li
Background The large volume and suboptimal image quality of portable chest X-rays (CXRs) as a result of the COVID-19 pandemic could post significant challenges for radiologists and frontline physicians. Deep-learning artificial intelligent (AI) methods have the potential to help improve diagnostic efficiency and accuracy for reading portable CXRs. Purpose The study aimed at developing an AI imaging analysis tool to classify COVID-19 lung infection based on portable CXRs. Materials and methods Public datasets of COVID-19 (N = 130), bacterial pneumonia (N = 145), non-COVID-19 viral pneumonia (N = 145), and normal (N = 138) CXRs were analyzed. Texture and morphological features were extracted. Five supervised machine-learning AI algorithms were used to classify COVID-19 from other conditions. Two-class and multi-class classification were performed. Statistical analysis was done using unpaired two-tailed t tests with unequal variance between groups. Performance of classification models ...
