Abstract
Segmentation which is identification of regions of interest (ROIs) in medical images is a very important step for image analysis in computer-aided diagnosis systems. Accurate segmentation of skin lesions images plays a vital role in efficient diagnosis of melanoma skin cancer. Diagnosis of melanoma cancer through the segmentation of skin lesions is a challenging task due to possible presence of noise and artefacts such as hairs, air or oil bubbles on the skin lesion images. Skin lesions images are also sometimes characterized with weak edges, irregular and fuzzy borders, marks, dark corners, skin lines and blood vessels on skin lesions. Recently, segmentation methods based on Fully Convolutional Encoder-Decoder Architecture (FCEDA) have achieved great success in medical images. This work presents automatic skin lesion segmentation method that is based on Fully Convolutional Encoder-Decoder Architecture. Two types of FCEDA namely U-Net and SegNet architectures, have been examined and utilized for segmentation of skin lesion images. The performance analysis of the two architectures have been conducted. Evaluation and comparison of these two architectures were also carried out. This work finds out and proposes possible improvements of these methods on the segmentation of skin lesions. It is also a systematic comparison of U-Net and SegNet models on the segmentation of skin lesion images. The paper discovers how deep learning methods can be explored using a supervised approach to get accurate results with less complexity possible. The models were evaluated on skin lesion challenge dataset in ISIC 2018 dermoscopic images archives.
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Adegun, A., Viriri, S. (2019). Fully Convolutional Encoder-Decoder Architecture (FCEDA) for Skin Lesions Segmentation. In: Nguyen, N., Chbeir, R., Exposito, E., Aniorté, P., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2019. Lecture Notes in Computer Science(), vol 11683. Springer, Cham. https://doi.org/10.1007/978-3-030-28377-3_35
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