Detection of Auto-Immune Disease using Deep Learning Techniques.

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.