Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Advanced Machine Learning Techniques for Detecting Irregularities in Skin Lesion Borders: Enhancing Early Skin Cancer Detection Subject The topic of the resource arc-based features; dermatogram analysis; ensemble learning; feature extraction; Skin cancer detection Description An account of the resource Dermatograms are pivotal in the early detection of skin cancer, a disease with significant mortality rates. This paper introduces a novel feature extraction method that captures irregularities in the boundaries of abnormal skin regions. Each raw dermatogram is converted into a binary mask image using an effective segmentation algorithm. The boundary of the lesion region is extracted from the mask. The boundary, together with the centroid of the lesion mask, is used to define a set of directional vectors. An Arc is defined using these directional vectors, and a new Arc feature is calculated based on the number of times the lesion boundary crosses the arc. The proposed Arc feature is evaluated using three standard skin lesion datasets: ISBI 2016, HAM10000, and PH2. Additionally, color features and Local Binary Pattern (LBP) features are implemented for comparison. Classical machine learning algorithms are employed to evaluate these features. Results indicate that for the ISBI 2016 and HAM10000 datasets, the Arc feature set demonstrates superior classification accuracy. In contrast, the PH2 dataset benefits more from the LBP feature. Comparative analysis with recent studies highlights the dependency of accuracy on datasets and classifiers, underscoring the necessity for models incorporating feature fusion and ensemble classifiers. The proposed method outperforms traditional color and texture features and shows competitive results against deep learning models, particularly in scenarios with limited computational resources. These findings suggest that the Arc feature is a promising approach for improving skin cancer detection, although further investigation is needed to fine-Tune performance, optimize classifier selection, and explore feature fusion strategies. 2024 World Scientific Publishing Company. Creator An entity primarily responsible for making the resource Raju G.; Nikesh P.; Resmi K.R.; Swain D.; Acharya B.; Gerogiannis V.C.; Kanavos A. Source A related resource from which the described resource is derived International Journal on Artificial Intelligence Tools Publisher An entity responsible for making the resource available World Scientific Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1142/S0218213024500246" target="_blank" rel="noreferrer noopener">https://doi.org/10.1142/S0218213024500246</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210081548&amp;doi=10.1142%2FS0218213024500246&amp;partnerID=40&amp;md5=300b51cec3d14f05b8837663f3f8a20f" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210081548&amp;doi=10.1142%2fS0218213024500246&amp;partnerID=40&amp;md5=300b51cec3d14f05b8837663f3f8a20f</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 2182130 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Raju G., Department of Computer Science and Engineering, Christ University, Bengaluru, Karnataka, India; Nikesh P., Department of Computer Science and Engineering, Government Engineering College, Wayanad, Kerala, India; Resmi K.R., Department of Computer Science and Engineering, Christ University, Bengaluru, Karnataka, India; Swain D., Department of Computer Science and Engineering, Pandit Deendayal Energy University, Gandhinagar, Gujarat, India; Acharya B., Department of Computer Engineering-AI, Marwadi University, Gujarat, Rajkot, India; Gerogiannis V.C., Department of Digital Systems, University of Thessaly, Larissa, Greece; Kanavos A., Department of Informatics, Ionian University, Corfu, Greece