Work

An Evaluation of Skin Lesion Segmentation Using Deep Learning Architectures

Year: 2024

Type: article

Abstract: Skin lesion segmentation for recognizing and defining the boundaries of skin lesions in images is proper for automated analysis of skin lesion images, especially for the early diagnosis and detection of skin cancers. Deep learning architectures are an efficient way to implement segmentation once a skin lesion dataset is provided with ground truth images. This study evaluates deep learning architectures on a hybrid dataset, including a private dataset collected from a hospital and a public ISIC dataset. Four different test cases exist in the analysis where the combinations of public and private datasets are used as train and test datasets. Experimental results include Unet, Unet++, DeepLabV3, DeepLabV3++, and FPN segmentation architectures. According to the comparative evaluations, mixed datasets, where public and private datasets were used together, provided the best results. The evaluations also show that the collected dataset with ground truth images provided promising results.

Source: Sakarya University Journal of Computer and Information Sciences

DOI: 10.35377/saucis...1543993

Authors: Gökçen Çetinel, Bekir Murat Aydın, Sevda Gül, Devrim Akgün, Rabia Öztaş Kara

Institution: Sakarya University

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: diamond

Funder: Türkiye Bilimsel ve Teknolojik Araştırma Kurumu