ArticleMediterranean journal of rheumatology2025
Detection of Auto-Immune Disease using Deep Learning Techniques.
Article in Mediterranean journal of rheumatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: The diagnosis of autoimmune disorders, particularly through the Anti-Nuclear Antibodies (ANA) Indirect Immunofluorescence (IIF) test utilising human epithelial type-2 (HEp-2) cells, presents a formidable challenge due to the subjective nature of pathologists' analysis. In response, this study proposes an innovative automated approach that integrates deep learning, advanced image processing, guided Hep-2 Cell, and mitotic cell instance segmentation. Methods: Leveraging the ICPR 2016 dataset for training and evaluation, this research encountered an initial challenge of dataset imbalance, with a significantly lower number of mitotic cells compared to HEp-2 Homogenous cells. To overcome this, data augmentation techniques were strategically employed to ensure a balanced representation. Results: In Experiment 1, the Detectron2 model achieved an overall mean Average Precision of 54% for segmentation masks and 55% for bounding boxes. In Experiment 2, the YOLOv8n model demonstrated an impressive overall Mean Average Precision score of 94% for bounding boxes and 93% for segmentation masks, showcasing its exceptional efficacy in detecting HEp-2 cells and mitotic cells. The instance segmentation provided a more granular analysis, revealing the count of cells in each class, further highlighting the model's proficiency in diagnosing autoimmune diseases. Conclusion: This study establishes a reliable and automated method for HEp-2 Homogenous cell detection, addressing the ongoing challenges in autoimmune disease diagnosis and contributing significantly to the ongoing revolution in this critical field.
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