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Classification of Non-Tumorous Facial Pigmentation Disorders Using Improved Smote and Transfer Learning.

, , , , , and . ICIP, page 220-224. IEEE, (2019)

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An improved image segmentation method for melasma severity assessment., , , , , and . DSP, page 1-5. IEEE, (2017)Hybrid threshold optimization between global image and local regions in image segmentation for melasma severity assessment., , , , , , , and . Multidimens. Syst. Signal Process., 28 (3): 977-994 (2017)Bridging the Gap Between Vitiligo Segmentation and Clinical Scores., , and . IEEE J. Biomed. Health Informatics, 28 (3): 1623-1634 (March 2024)Machine Learning Assisted Handheld Confocal Raman Micro-Spectroscopy for Identification of Clinically Relevant Atopic Eczema Biomarkers., , , , , , , , and . Sensors, 22 (13): 4674 (2022)Classification of Non-Tumorous Facial Pigmentation Disorders Using Improved Smote and Transfer Learning., , , , , and . ICIP, page 220-224. IEEE, (2019)Classification of non-tumorous skin pigmentation disorders using voting based probabilistic linear discriminant analysis., , , , , , and . Comput. Biol. Medicine, (2018)Classification of Non-Tumorous Facial Pigmentation Disorders using Deep Learning and SMOTE., , , , , and . ISCAS, page 1-5. IEEE, (2019)Reaction-diffusion based level set method with local entropy thresholding for melasma image segmentation., , , , , , and . ICARCV, page 1-5. IEEE, (2016)