Deep Learning Based COVID-19 Detection Using Medical Images: Is Insufficient Data Handled Well?

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Computer-Aided Diagnosis

Background:

• Deep learning models are increasingly used for automated COVID-19 detection from medical imaging.
• A significant challenge in developing these models is the limited availability of diverse and comprehensive datasets.
• This data insufficiency can hinder the generalization and accuracy of deep learning algorithms.

Purpose of the Study:

• To provide a comprehensive overview of the data insufficiency issue in deep learning-based COVID-19 detection.
• To analyze existing medical datasets, including CT and X-ray images, relevant for COVID-19 detection frameworks.
• To discuss essential data handling techniques and propose advanced strategies to mitigate data scarcity.

Main Methods:

• Extensive review and analysis of publicly available COVID-19 medical image datasets (CT and X-ray).
• Detailed discussion of various data augmentation and preprocessing techniques applicable to medical imaging.
• Exploration of model modification strategies to enhance robustness against limited data.

Main Results:

• Identification of key characteristics and limitations of current COVID-19 imaging datasets.
• Evaluation of the effectiveness of standard data handling techniques in the context of deep learning for medical diagnosis.
• Demonstration of how advanced techniques can improve model performance despite data scarcity.

Conclusions:

• Addressing data insufficiency is critical for reliable deep learning-based COVID-19 detection.
• Strategic application of advanced data handling and model modification techniques can overcome dataset limitations.
• Further research into dataset curation and innovative deep learning approaches is warranted for improved diagnostic accuracy.