AntiNuclear Antibody Pattern Classification Using CNN with Small Dataset

Antinuclear antibody patterns are used as an important screening technique to diagnose autoimmune disorders. The rising prevalence of autoimmune conditions, such as connective tissue diseases, has resulted in an increase in the production of antinuclear antibodies (ANA). Unavailability of expert pathologist delay the analysis and interpretation of ANA patterns in many places. Automated analysis of ANA pattern can reduce the time of pathological investigation and help doctors to plan for the treatment. This work proposes a convolutional neural network based model that can classify the ANA pattern into four different categories - mitotic, nuclear, cytoplasmic, and negative. The model trained with relatively fewer number of samples has performed satisfactorily while being trained and tested with ANA dataset. It exhibits a relatively good F1 score of 0.97.