Open AccessThe Diagnosis of COVID-19 through X-ray Images via Transfer Learning and Fine-Tuned Dense Layer on Pipeline
Author(s) -
Amiir Haamzah Mohamed Ismail,
Mohd Azraai Mohd Razman,
Ismail Mohd Khairuddin,
Muhammad Amirul Abdullah,
Rabiu Muazu Musa,
Anwar P. P. Abdul Majeed
Publication year - 2021
Publication title -
mekatronika : journal of intelligent manufacturing and mechatronics
Language(s) - English
Resource type - Journals
ISSN - 2637-0883
DOI - 10.15282/mekatronika.v3i2.7161
Subject(s) - transfer of learning , hyperparameter , pipeline (software) , computer science , artificial intelligence , covid-19 , dropout (neural networks) , deep learning , artificial neural network , layer (electronics) , pattern recognition (psychology) , task (project management) , machine learning , feature extraction , feature (linguistics) , materials science , medicine , engineering , nanotechnology , linguistics , philosophy , disease , systems engineering , pathology , infectious disease (medical specialty) , programming language
X-ray is used in medical treatment as a method to diagnose the human body internally from diseases. Nevertheless, the development in machine learning technologies for pattern recognition have allowed machine learning of diagnosing diseases from chest X-ray images. One such diseases that are able to be detected by using X-ray is the COVID-19 coronavirus. This research investigates the diagnosis of COVID-19 through X-ray images by using transfer learning and fine-tuning of the fully connected layer. Next, hyperparameters such as dropout, p, number of neurons, and activation functions are investigated on which combinations of these hyperparameters will yield the highest classification accuracy model. InceptionV3 which is one of the common neural network is used for feature extraction from chest X-ray images. Subsequently, the loss and accuracy graphs are used to find the pipeline which performs the best in classification task. The findings in this research will open new possibilities in screening method for COVID-19.