CapsDeMM: Capsule network for Detection of Munro's Microabscess in skin biopsy images

TL;DR

This paper introduces CapsDeMM, a capsule network-based system that automatically detects Munro's Microabscess in skin biopsy images, aiding psoriasis diagnosis with reduced parameters and effective handling of large images.

Contribution

The novel capsule network design significantly reduces parameters while maintaining performance, and the system effectively processes giga-pixel images for clinical application.

Findings

Achieved high accuracy in detecting neutrophils in biopsy images.

Reduced model complexity without performance loss.

Demonstrated system's practicality on real-world datasets.

Abstract

This paper presents an approach for automatic detection of Munro's Microabscess in stratum corneum (SC) of human skin biopsy in order to realize a machine assisted diagnosis of Psoriasis. The challenge of detecting neutrophils in presence of nucleated cells is solved using the recent advances of deep learning algorithms. Separation of SC layer, extraction of patches from the layer followed by classification of patches with respect to presence or absence of neutrophils form the basis of the overall approach which is effected through an integration of a U-Net based segmentation network and a capsule network for classification. The novel design of the present capsule net leads to a drastic reduction in the number of parameters without any noticeable compromise in the overall performance. The research further addresses the challenge of dealing with Mega-pixel images (in 10X) vis-a-vis Giga-pixel ones (in 40X). The promising result coming out of an experiment on a dataset consisting of 273 real-life images shows that a practical system is possible based on the present research. The implementation of our system is available at https://github.com/Anabik/CapsDeMM.