Computer-aided diagnosis of auto-immune disease using capsule neural network

Manual analysis of the indirect-immunofluorescence (IIF) human epithelial cell Type-2 (HEp-2) cell image for the diagnosis of an auto-immune disease is a subjective and time-consuming process, and it is also prone to human-errors. The present work proposes an automatic capsule neural network (CapsNet) based framework for HEp-2 cell image classification to compensate for the deficiencies present in the prominent convolution neural network (CNN) based frameworks. In CNNs, the spatial relationship between the features present in the anti-nuclear antibodies (ANA) patterns, found in the IIF HEp-2 cell image (ANA-IIF image) is lost which increases the chance of detection of false-positives. In the proposed CapsNet based model, the max-pooling layer has been replaced with advanced dynamic routing algorithm and scalar outputs are replaced with the vector output, thus the richer representation of the same feature without the loss of spatial relationship with respect to the other features are made possible. The proposed framework recognizes ANA-IIF images with an average accuracy of 95.00% for 10-fold cross-validations. The experimental result also shows that the proposed model performs better than the other CNN based classification models for human epithelial cell image classification task.