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Classification of papulo-squamous skin diseases using image analysis.

H M Mashaly¹, N A Masood, Abdalla S A Mohamed

¹Department of Dermatology, Faculty of Medicine, Cairo University, Cairo, Egypt. hebamash2@gmail.com

Skin Research and Technology : Official Journal of International Society for Bioengineering and the Skin (ISBS) [And] International Society for Digital Imaging of Skin (ISDIS) [And] International Society for Skin Imaging (ISSI)

|February 23, 2011

Summary

This study shows that the rough sets approach is superior for classifying papulo-squamous skin diseases. This computer-aided system achieved 96.7% accuracy in differentiating these common skin conditions.

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Area of Science:

Dermatology
Computer Science
Medical Imaging

Background:

Papulo-squamous skin diseases present similar erythematous, scaly lesions, making clinical differentiation challenging.
Accurate diagnosis relies heavily on dermatologist experience and common sense.
A need exists for objective tools to aid in classifying these variable conditions.

Purpose of the Study:

To evaluate a computer-based image analysis system for classifying common papulo-squamous skin diseases.
To assess the system's effectiveness as a diagnostic aid for dermatologists.

Main Methods:

A dataset of 300 images, with 50 each of psoriasis, lichen planus, atopic dermatitis, seborrheic dermatitis, pityriasis rosea, and pityriasis rubra pilaris, was used.
The image analysis involved segmentation, feature extraction, and classification processes.
Various algorithms including rough sets, K-means clustering, and topological derivatives were compared.

Main Results:

The rough sets approach demonstrated the highest accuracy and sensitivity in image segmentation compared to topological derivative, K-means, and watershed methods.
A rule-based classifier utilizing rough sets achieved the highest classification accuracy at 96.7% for the six disease groups.
Rough sets outperformed other classification techniques like K-means, fuzzy c-means, and K-nearest neighbor.

Conclusions:

The rough sets approach is highly effective for both segmentation and classification of papulo-squamous skin diseases.
This computational method offers a superior alternative to traditional techniques for diagnosing these skin conditions.
Computer-based image analysis using rough sets shows significant potential as a diagnostic aid in dermatology.