Mr. Adel Alshamili Profile

Adel Alshamili

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Proceedings Article | 7 June 2024 Poster + Paper

Deep learning architecture: an application for skin lesion segmentation-based polar image transformations on dermoscopy images

Redha Ali, Almabrok Essa, Elmoatazbellah Ben Omar, Adel Alshamili, Fatmah Ali, Russell Hardie

Proceedings Volume 13040, 130400J (2024) https://doi.org/10.1117/12.3014731

KEYWORDS: Image segmentation, Skin, Education and training, Transformers, Deep learning, Data modeling, Performance modeling, Medical imaging, Skin cancer, RGB color model

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Accurately segmenting skin lesions from dermoscopy images is crucial for improving the quantitative analysis of skin cancer. However, segmenting melanoma automatically is difficult due to the significant variation in melanoma and unclear boundaries of the lesion areas. While Convolutional Neural Networks (CNNs) have made impressive progress in this area, most existing solutions need help to effectively capture global dependencies resulting from limited receptive fields. Recently, transformers have emerged as a promising tool for modeling global context by using a powerful global and local attention mechanism. In this paper, we investigated the effectiveness of various deep learning, including CNN-based and transform-based approaches, for the segmentation of skin lesions on dermoscopy images. We also studied and compared the performance of transfer learning algorithms developed based on well-established encoders such as Swin Transformer, Mix-Transformer, Vision Transformer, ResNet, VGG-16, and DenseNet. Our proposed approach involves training a neural network on polar transformations of the original dataset, with the polar origin set to the object’s center point. This simplifies the segmentation and localization tasks and reduces dimensionality, making it easier for the network to converge. The ISIC 2018 datasets containing 2,594 dermoscopy images with their ground truth segmentation masks was used in the evaluation of our approach for skin lesion segmentation tasks. This dataset was randomly split into 70%, 10%, and 20% groups for training, validation, and testing purposes. The experimental results showed that when we used polar transformations as a pre-processing step, the CNN-based and transform-based approaches generally improved the models efficiency across dataset.

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