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

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