Work

Hairless Image Preprocessing for Accurate Skin Lesion Detection and Segmentation

Year: 2026

Type: article

Abstract: Skin cancer is a widespread and fatal disease in which early and accurate detection is an important aspect for effective treatment. The issues that arise when performing automated analysis of dermatoscopic images include artifacts such as hair, low contrast, and irregular edges of lesions that interfere with segmentation and classification. This study proposes an automated image preprocessing pipeline designed to remove artifacts while saving lesion texture and boundary. The method combines various computer vision methods and processes to produce a hairless dermatoscopic image of the sample, and lesion segmentation is subsequently performed using the HSV color space and binary masking. The effectiveness of the proposed preprocessing approach is evaluated using five state-of-the-art models: VGG16, ResNet50, InceptionV3, EfficientNet-B4, and DenseNet121.

Source: Applied Sciences

DOI: 10.3390/app16041819

Authors: Muhammet Pasaoglu, Irem Demirkan

Institutions: Bahçeşehir University, Centre National de la Recherche Scientifique, Laboratoire de Physique des deux infinis Bordeaux

Language: English

Topic: Cutaneous Melanoma Detection and Management

Subfield: Oncology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,896