AutoMID: A Novel Framework For Automated Computer Aided Diagnosis Of Medical Images
6th International Conference on Advances in Artificial Intelligence, ICAAI 2022
; : 74-80, 2022.
Article
in English
| Scopus | ID: covidwho-2236972
ABSTRACT
Machine Learning, a subtype of AI, enables computers to mimic human behavior without explicit programming. Machine learning models aren't used very often in diagnostic imaging because there isn't enough knowledge and resources to do so. Hence, this study aims to apply automated machine learning to the diagnosis of medical images to make machine learning more accessible to non-experts. In this study, a dataset containing 2313 images each of covid-19, pneumonia and normal chest x-rays were selected and divided into testing, training, and validation datasets. The AutoGluon library was used to train and produce a model that would classify an input image and infer the probable diagnosis from the diseases it was trained upon. This study can prove that applying hyperparameter optimization and neural architecture search is able to produce high accuracy models for medical image diagnosis. © 2022 Association for Computing Machinery.
Automated Machine Learning; Computer Aided Diagnosis; Hyperparameter Tuning; Medical Images; Neural Architecture Search; Automation; Behavioral research; Computer aided instruction; Computer programming; Machine learning; Medical imaging; Statistical tests; Automated machines; Human behaviors; Hyper-parameter; Machine learning models; Machine-learning; Medical image; Neural architectures
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Diagnostic study
/
Prognostic study
Language:
English
Journal:
6th International Conference on Advances in Artificial Intelligence, ICAAI 2022
Year:
2022
Document Type:
Article
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