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Using Adaptive Thresholding and Skewness Correction to Detect Gray Areas in Melanoma In Situ Images.

, , , , , , and . IEEE Trans. Instrum. Meas., 61 (7): 1839-1847 (2012)

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ChimeraNet: U-Net for Hair Detection in Dermoscopic Skin Lesion Images., , , , , , , , and . J. Digit. Imaging, 36 (2): 526-535 (April 2023)Deep learning-based dot and globule segmentation with pixel and blob-based metrics for evaluation., , , , , , , , , and 1 other author(s). Intell. Syst. Appl., (2022)Deep Learning and Handcrafted Method Fusion: Higher Diagnostic Accuracy for Melanoma Dermoscopy Images., , , , , , , , , and . IEEE J. Biomed. Health Informatics, 23 (4): 1385-1391 (2019)Fuzzy logic color detection: Blue areas in melanoma dermoscopy images., , , , , , , and . Comput. Medical Imaging Graph., 38 (5): 403-410 (2014)Using Adaptive Thresholding and Skewness Correction to Detect Gray Areas in Melanoma In Situ Images., , , , , , and . IEEE Trans. Instrum. Meas., 61 (7): 1839-1847 (2012)Increasing Melanoma Diagnostic Confidence: Forcing the Convolutional Network to Learn from the Lesion., , , , , and . CoRR, (2023)Medical Image Processing in the Age of Deep Learning - Is There Still Room for Conventional Medical Image Processing Techniques?., , and . VISIGRAPP (4: VISAPP), page 306-311. SciTePress, (2017)Adaptable Ring for Vision-Based Measurements and Shape Analysis., , , , , , and . IEEE Trans. Instrum. Meas., 66 (4): 746-756 (2017)Skin Lesion Segmentation in Dermoscopic Images with Noisy Data., , , , and . J. Digit. Imaging, 36 (4): 1712-1722 (August 2023)Discrimination of squamous cell carcinoma in situ from seborrheic keratosis by color analysis techniques requires information from scale, scale-crust and surrounding areas in dermoscopy images., , , , , , and . Comput. Biol. Medicine, 42 (12): 1165-1169 (2012)