Comparison of Segmentation Methods for Melanoma Diagnosis in Dermoscopy Images

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Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms (2013, background): “Thus, we investigate this topic in detail to come up with a guideline for future research.”
Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks With Jaccard Distance (2017, result): “have been discussed and compared in many articles such as [5], [8], [9].”
Dermoscopic Image Segmentation via Multistage Fully Convolutional Networks (2017, methodology): “Semi-automatic methods require user initialization of the segmentation process, such as through seed selection [12] or con-”
Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks (2017, background): “Dermoscopy assessment is widely used in the diagnosis of melanoma and obtains much higher accuracy rates than evaluation by naked eyes [6].”
Understanding Deep Learning Techniques for Image Segmentation (2019, background): “ral applications in the real world. In medical image processing [123] as well we need to localize various abnormalities 2 like aneurysms [48], tumors [145], cancerous elements like melanoma detection [189], or specic organs during surgeries [206]. Another domain where segmentation is important is su”
Two Systems for the Detection of Melanomas in Dermoscopy Images Using Texture and Color Features (2014, methodology): “A comparison among several methods can be found in [40].”