A NOVEL INTELLIGENT APPROACH FOR MELANOMA IDENTIFICATION BASED ON PIGMENT NETWORK FEATURES

Abstract

Early identification of melanoma, one of the most aggressive and life-threatening forms of skin cancer, plays a vital role in improving patient survival and treatment outcomes. Despite significant advancements in medical imaging, accurately distinguishing malignant skin lesions from dermoscopic images remains a complex and demanding task. Among the various diagnostic features, the pigment network is considered a key visual indicator for melanoma assessment. However, reliable extraction of pigment network patterns is often hindered by challenges such as image noise, uneven illumination, and the presence of hair artifacts, which can mask critical lesion characteristics and reduce diagnostic accuracy.

To overcome these limitations, this paper introduces a set of novel image processing techniques designed for computer-aided pigment network detection. The proposed approach focuses on improving image quality and enhancing diagnostically relevant features to support more accurate melanoma analysis. The process begins with an effective preprocessing stage that applies advanced filtering methods to suppress noise and remove unwanted hair artifacts from dermoscopic images. This step significantly enhances visual clarity and prepares the images for further analysis.

Following preprocessing, the enhanced images are transformed into binary representations, and corresponding binary masks are generated to isolate regions of interest. This conversion simplifies complex image structures and enables more precise identification of pigment network patterns. In the final stage, the system detects pigment networks and extracts essential quantitative features, including measurements such as diameter and radius, which are important indicators for evaluating melanoma presence.

Experimental results demonstrate that the proposed method performs competitively when compared with existing state-of-the-art techniques. The system achieves an average precision of 0.89 and an average recall of 0.87, with an overall classification accuracy of 88.5%. These results indicate that the proposed framework offers a reliable, efficient, and practical solution for automated pigment network detection and has strong potential to support early melanoma diagnosis in clinical settings.