Hairless Image Preprocessing for Accurate Skin Lesion Detection and Segmentation
Source abstract
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: crossref · Quality: baseline
Research context
OpenAlex topics: Cutaneous Melanoma Detection and Management · Nonmelanoma Skin Cancer Studies · AI in cancer detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Engineering, Bahcesehir University, Yildiz, Ciragan Cd., 34349 Istanbul, Türkiye
Department of Biomedical Engineering, Bahcesehir University, Yildiz, Ciragan Cd., 34349 Istanbul, Türkiye
Laboratoire de Physique des Deux Infinis de Bordeaux, Centre National de la Recherche Scientifique (CNRS), 33170 Gradignan, France
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