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From traditional to deep learning methods for skin lesion segmentation: A Literature Review

Scientific African 2025年

作者： Abdelkrim Hammimou Hassan Ezzahori Abdelghani Boudaoud Mounaim Aqil Engineering and Applied Physics Team Superior School of Technology Sultan Moulay Slimane University Beni Mellal Morocco

Due to their similarity, skin lesions are challenging for early and precise diagnosis, and segmenting them manually is a laborious and time-consuming task. Therefore, several methods, including traditional and artificial intelligence methods, have been proposed for segmenting the skin lesions automatically. The majority of recent reviews, however, have overlooked the traditional methods and instead only briefly discussed their advantages and disadvantages. Consequently, this review aims to provide a thorough overview of this field and offer insights about the role and performance of the traditional methods in the era of artificial intelligence. We collected 128 peer-reviewed and high-quality publications using inclusion and exclusion criteria. These publications have been selected from four well-known databases, IEEE Xplore, ScienceDirect, Springer, and MDPI, and classified systematically into three categories: traditional, artificial intelligence, and hybrid methods. The main contributions of this paper are: (1) Exploring the most used traditional methods and performance enhancement techniques. (2) Providing the State of the art of the convolutional neural network and deep learning methods. (3) Highlighting the role of traditional methods in hybrid frameworks. (4) Providing an extensive performance analysis and discussing the computational cost and real-world applicability challenges associated with segmentation methods. The results highlight the superiority of deep learning methods over traditional methods, while also recognizing the relevance of traditional methods like Active contours and clustering as trusted tools for skin lesion segmentation. These methods benefit from improvements such as ensembling techniques and optimization algorithms. Additionally, 9% of reviewed papers were motivated by the combination of benefits from traditional methods and deep learning methods to develop more robust models. In conclusion, significant challenges remain in developing

关键词： Skin lesion segmentation Automatic Segmentation Traditional methods Convolutional neural network Deep Learning Ensembling methods optimization algorithms Hybrid frameworks ABCDE Asymmetry, Border, Color, Diameter, and Evolution method AC Active contour ACC Accuracy ACO Ant colony optimization algorithm AI Artificial intelligence AK Actinic keratosis BCC Basal cell carcinoma BUZOA buzzards optimization algorithm CAD Computer aided diagnosis CNN Convolutional neural network COVIDOA Coronavirus optimization algorithm CPU Central Processing Unit CST Color space transformation DC Dice coefficient DF Dermatofibroma FCM Fuzzy c-means FLOP Floating-point operation FN False negative FP False positive FPS Frames per second FRCN Full-resolution convolution network GA Genetic algorithm GAN Generative adversarial networks GPU Graphical processing unit IoU Intersection over union IPO interior-point optimization JI Jaccard index KM K-means MAC Multiply-accumulate operation Mel Melanoma Nev Nevus PM Performance metric PSO Particle swarm optimization ROI Region of interest SCC Squamous cell carcinoma SENS Sensitivity SFS Stochastic fractal search SGO Social group optimization SIFT Scale-invariant feature transform SK Seborrheic keratosis SLC Skin lesion classification SLS Skin lesion segmentation SLIC Simple linear iterative clustering SPEC Specificity SSL Self supervised learning Swin-transformer Shifted window-based vision transformers TN True negative TP True positive WOA Whale optimization algorithm

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