Rajendra S. Sonawane

TL;DR: A first comparative performance study of its kind using principal component analysis (PCA) based CADx system for psoriasis risk stratification and image classification utilizing 11 higher order spectra (HOS) features, 60 texture features, and 86 color feature sets and their seven combinations is presented.

TL;DR: The study demonstrates a fully novel model of segmentation embedded with risk assessment, and uses the combination of SVM and FDR as the optimal pRAS system and yielded a classification accuracy of 99.84% using cross-validation protocol.

TL;DR: A dermatology CADx system to automatically classify dermatology images into psoriatic lesion and healthy skin using an online system is presented and can demonstrate the reliability and consistency factor by showing the monotonously rising accuracy with increase in data size.

TL;DR: An automated psoriasis lesion segmentation method based on a modified U-Net architecture that provides accelerated training by reducing the covariate shift through the implementation of batch normalization and is capable of segmenting the lesion even in challenging cases such as under poor acquisition conditions and in the presence of artifacts.

TL;DR: An automated Psoriasis computer-aided diagnosis (pCAD) system for classification of psoriasis skin images into psoriatic lesion and healthy skin, which solves the two major challenges: fulfills the color feature requirements and selects the powerful dominant color features while retaining high classification accuracy.