A Neuroimaging Yolov8-Based Cad Framework for Anosmia Grading in Covid-19

Document Type

Conference Proceeding

Source of Publication

2024 IEEE International Conference on Image Processing (ICIP)

Publication Date

10-30-2024

Abstract

COVID-19, a respiratory illness caused by SARS-CoV-2, has brought attention to a common symptom: loss of smell and taste. Anosmia, a prevalent symptom of COVID-19, varies in severity from mild to severe, necessitating accurate diagnostic tools. The study proposes a novel framework for predicting COVID-19 anosmia severity utilizing YOLOv8 for classification and EigenCAM for interpretability. YOLOv8, optimized for object detection, is adapted for classification tasks using advanced architectural enhancements and mosaic augmentation. EigenCAM provides interpretability by highlighting image regions crucial for predictions, aiding clinical decision-making. Evaluation across multiple YOLOv8 model sizes using DTI and FLAIR modalities reveals robust performance, with the Large model excelling in DTI and the Nano model in FLAIR. Compared to our previous work, the framework significantly enhances accuracy and interpretability in predicting anosmia severity, marking a substantial advancement in medical image analysis. This study underscores the potential of deep learning for precise and interpretable medical diagnostics, offering insights into anosmia severity prediction.

ISBN

979-8-3503-4939-9

Publisher

IEEE

Volume

00

First Page

2951

Last Page

2956

Disciplines

Computer Sciences | Medicine and Health Sciences

Keywords

Neuroimaging, YOLOv8, Anosmia Grading, COVID-19, EigenCAM

Indexed in Scopus

no

Open Access

no

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