Pigment Network Detection and Classification in Dermoscopic Images Using Directional Imaging Algorithms and Convolutional Neural Networks

TL;DR

This study develops an automated method combining directional imaging algorithms and CNNs to detect and classify pigment networks in dermoscopic images, aiding early melanoma diagnosis with high accuracy.

Contribution

It introduces a novel directional imaging algorithm for pigment network detection and a lightweight CNN classifier tailored for limited datasets, outperforming existing methods.

Findings

Directional imaging algorithm achieved 96-100% success rate.

CNN classifier attained 90% accuracy, sensitivity, and 89% specificity.

Proposed method outperforms state-of-the-art techniques.

Abstract

Early diagnosis of melanoma, which can save thousands of lives, relies heavily on the analysis of dermoscopic images. One crucial diagnostic criterion is the identification of unusual pigment network (PN). However, distinguishing between regular (typical) and irregular (atypical) PN is challenging. This study aims to automate the PN detection process using a directional imaging algorithm and classify PN types using machine learning classifiers. The directional imaging algorithm incorporates Principal Component Analysis (PCA), contrast enhancement, filtering, and noise reduction. Applied to the PH2 dataset, this algorithm achieved a 96% success rate, which increased to 100% after pixel intensity adjustments. We created a new dataset containing only PN images from these results. We then employed two classifiers, Convolutional Neural Network (CNN) and Bag of Features (BoF), to categorize PN into atypical and typical classes. Given the limited dataset of 200 images, a simple and effective CNN was designed, featuring two convolutional layers and two batch normalization layers. The proposed CNN achieved 90% accuracy, 90% sensitivity, and 89% specificity. When compared to state-of-the-art methods, our CNN demonstrated superior performance. Our study highlights the potential of the proposed CNN model for effective PN classification, suggesting future research should focus on expanding datasets and incorporating additional dermatological features to further enhance melanoma diagnosis.