Skin Lesion Segmentation and Classification for ISIC 2018 Using Traditional Classifiers with Hand-Crafted Features
This work addresses melanoma detection in medical imaging, but it is incremental as it applies existing methods to a known dataset.
The paper tackled skin lesion segmentation and classification for melanoma detection using traditional classifiers with hand-crafted features, achieving results submitted for ISIC 2018 tasks but without providing specific performance numbers.
This paper provides the required description of the methods used to obtain submitted results for Task1 and Task 3 of ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection. The results have been created by a team of researchers at the University of Dayton Signal and Image Processing Lab. In this submission, traditional classifiers with hand-crafted features are utilized for Task 1 and Task 3. Our team is providing additional separate submissions using deep learning methods for comparison.