Deep residual network with regularised fisher framework for detection of melanoma

Abstract

Of all the skin cancer that is prevalent, melanoma has the highest mortality rates. Melanoma becomes life threatening when it penetrates deep into the dermis layer unless detected at an early stage, it becomes fatal since it has a tendency to migrate to other parts of our body. This study presents an automated non‐invasive methodology to assist the clinicians and dermatologists for detection of melanoma. Unlike conventional computational methods which require (expensive) domain expertise for segmentation and hand crafted feature computation and/or selection, a deep convolutional neural network‐based regularised discriminant learning framework which extracts low‐dimensional discriminative features for melanoma detection is proposed. Their approach minimises the whole of within‐class variance information and maximises the total class variance information. The importance of various subspaces arising in the within‐class scatter matrix followed by dimensionality reduction using total class variance information is analysed for melanoma detection. Experimental results on ISBI 2016, MED‐NODE, PH2 and the recent ISBI 2017 databases show the efficacy of their proposed approach as compared to other state‐of‐the‐art methodologies.

Keywords

• within-class variance information
• total class variance information
• melanoma detection
• residual network
• regularised fisher framework
• skin cancer