Multi-objective Genetic Algorithm Based Deep Learning Model for Automated COVID-19 Detection Using Medical Image Data

As a software component which is capable of learning in an autonomous way, software agent should have the capability of learning in a dynamic environment. Genetic Algorithm has a wide perspective in the machine learning because of its unique characteristic (e.g. dynamic adaptability, self-organization, global convergence and robustness). But when applying GA to agent’s dynamic learning model, it encounters a series of problem. In this paper, a Modifided Multi-Objective Genetic Algorithm(MMOGA) will be introduced to solve these problems. Finally, an Agent’s Dynamic learning model based on a MMOGA which has the flexible dynamic learning capability, better global convergence and performance, will be introduced.

Download Full-text

Amplification of pixels in medical image data for segmentation via deep learning object-oriented approach

International Journal of Advanced Technology and Engineering Exploration ◽

10.19101/ijatee.2020.s1762117 ◽

2021 ◽

Vol 8 (74) ◽

pp. 82-90

Author(s):

Ahmad Firdaus Ahmad Fadzil ◽

Noor Elaiza Abd Khalid ◽

Shafaf Ibrahim

Keyword(s):

Deep Learning ◽

Medical Image ◽

Object Oriented ◽

Image Data ◽

Learning Object ◽

Medical Image Data ◽

Object Oriented Approach ◽

Oriented Approach

Download Full-text

Multi-Objective Genetic Algorithm Based Deep Learning Model for Automated COVID-19 Detection using Medical Image Data

10.21203/rs.3.rs-311776/v1 ◽

2021 ◽

Author(s):

Shrey Bansal ◽

Mukul Singh ◽

Rahul Dubey ◽

Bijaya Ketan Panigrahi

Keyword(s):

Genetic Algorithm ◽

Transmission Rate ◽

Binary Classification ◽

Feature Learning ◽

Image Data ◽

Support Vector ◽

Current Standard ◽

Multi Objective ◽

Multi Objective Genetic Algorithm ◽

Novel Coronavirus

Abstract In early 2020, the world is amid a significant pandemic due to the novel coronavirus disease outbreak, commonly called the COVID-19. Coronavirus is a lung infection disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 virus (SARS-CoV-2). Because of its high transmission rate, it is crucial to detect cases as soon as possible to effectively control the spread of this pandemic and treat patients in the early stages. RT-PCR-based kits are the current standard kits used for COVID-19 diagnosis, but these tests take much time despite their high precision. A faster automated diagnostic tool is required for the effective screening of COVID-19. In this study, a new semi-supervised feature learning technique is proposed to screen COVID-19 patients using chest CT Scans. The model proposed in this study uses a three-step architecture, consisting of a Convolutional Autoencoder based unsupervised feature extractor, a Multi-Objective Genetic Algorithm based feature selector, and a Bagging Ensemble of Support Vector Machines(SVMs) based classifier. The Autoencoder generates a diverse set of features from the images, and an optimal subset, free of redundant and irrelevant features, is selected by the evolutionary selector. The Ensemble of SVMs then performs the binary classification of the features. The proposed architecture has been designed to provide precise and robust diagnostics for binary classification (COVID vs.nonCOVID). A Dataset of 1252 COVID-19 CT scan images, collected from 60 patients, has been used to train and evaluate the model. The experimental results prove the superiority of the proposed methodology in comparison to existing methods.

Download Full-text

Transfer learning with chest X-rays for ER patient classification

Scientific Reports ◽

10.1038/s41598-020-78060-4 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Jonathan Stubblefield ◽

Mitchell Hervert ◽

Jason L. Causey ◽

Jake A. Qualls ◽

Wei Dong ◽

...

Keyword(s):

Deep Learning ◽

Current Model ◽

Image Data ◽

Learning Model ◽

Image Features ◽

Clinical Feature ◽

Patient Classification ◽

X Rays ◽

Data Set ◽

Deep Learning Model

AbstractOne of the challenges with urgent evaluation of patients with acute respiratory distress syndrome (ARDS) in the emergency room (ER) is distinguishing between cardiac vs infectious etiologies for their pulmonary findings. We conducted a retrospective study with the collected data of 171 ER patients. ER patient classification for cardiac and infection causes was evaluated with clinical data and chest X-ray image data. We show that a deep-learning model trained with an external image data set can be used to extract image features and improve the classification accuracy of a data set that does not contain enough image data to train a deep-learning model. An analysis of clinical feature importance was performed to identify the most important clinical features for ER patient classification. The current model is publicly available with an interface at the web link: http://nbttranslationalresearch.org/.

Download Full-text