🤖 AI Summary
This work proposes a two-stage method to automatically identify regular and irregular pigment networks (PN) in dermoscopic images for aiding early melanoma diagnosis. In the first stage, PN regions are precisely localized by integrating directional imaging, principal component analysis (PCA), and contrast enhancement. The second stage employs a lightweight convolutional neural network (CNN) to classify typical versus atypical PN patterns. Evaluated on a small dataset of 200 images, the approach achieves 90% accuracy, 90% sensitivity, and 89% specificity, with a 100% PN detection rate—significantly outperforming existing techniques under limited data conditions. By effectively combining classical image processing with deep learning, the method enhances both the automation and reliability of melanoma screening.