# DMpDP: a Diagnostic Multiple-patient DermoFeature Profile store-and-forward teledermoscopy system

## TL;DR

This paper introduces DMpDP, a teledermoscopy system that compresses dermoscopy images into feature profiles for efficient transmission and diagnosis.

## Contribution

The novel DMpDP system uses weighted intensity-difference frequency and morphological features to enable efficient store-and-forward teledermoscopy.

## Key findings

- The sequential-based DMpDP achieved the highest classification accuracy under -5 dB noise.
- A signal-to-noise ratio of 98.79% was realized using β=100 and spectral subtraction filtering.
- The system effectively transmits 248 DPs with high diagnostic accuracy and signal quality.

## Abstract

Telehealth demand is rapidly growing along with the necessity of providing wide-scale services covering multiple patients at the same time. In this work, the development of a store-and-forward (SAF) teledermoscopy system was considered. The dermoFeatures profile (DP) was proposed to decrease the size of the original dermoscopy image using its most significant features in the form of a newly generated diagonal alignment to generate a small-sized image DP, which is based on the extraction of a weighted intensity-difference frequency (WIDF) features along with morphological features (MOFs). These DPs were assembled to establish a Diagnostic Multiple-patient DermoFeature Profile (DMpDP). Different arrangements are proposed, namely the horizontally aligned, the diagonal-based, and the sequential-based DMpDPs to support the SAF systems. The DMpDPs are then embedded in a recorded patient-information signal (RPS) using a weight factor β to boost the transmitted patient-information signal. The effect of the different transform domains, β values, and number of DPs within the DMpDP were investigated in terms of the diagnostic classification accuracy at the receiver based on the extracted DPs, along with the recorded signal quality evaluation metrics of the recovered RPS. The sequential-based DMpDP achieved the highest classification accuracy, under − 5 dB additive white Gaussian noise, with a realized signal-to-noise ratio of 98.79% during the transmission of 248 DPs using β = 100, and spectral subtraction filtering.