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 Book Chapter Book Chapter Faculty Publications- Book Chapter 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 Ensembled convolutional neural network for multi-class skin cancer detection Subject The topic of the resource Artificial intelligence; Classification; HAM10000 dataset; Medical image processing; Multi-class skin cancer Description An account of the resource A skin cancer diagnosis is critically important in medical image processing. The role of dermoscopy and dermatologists is inevitable in skin cancer diagnosis. But, considering the time constraints on diagnosing patients on time, even medical experts need computer-assisted methods to automate the diagnosis process with a higher accuracy rate and with good performance. Such computer-assisted methods with induced artificial intelligence (AI) algorithms are gaining significance. The challenging task of medical image processing is finding benign/malignant pigmented skin lesions after the input image of patients. To identify this difference, AI-based classification algorithms shall be deployed. During the implementation of such algorithms, several performance aspects are evaluated. Once the best such algorithm is identified and evaluated for its performance attributes, it shall be deployed to assist dermatologists. This book chapter explains such a novel multiclass skin cancer classification algorithm. The proposed algorithm uses the best of the attributes and parameters of a deep convolutional neural network (CNN) to give the best-ever enactment among similar existing algorithms. The result achievement of the developed deep CNN based multi-class skin cancer classification algorithm (DCNN-MSCCA) is demonstrated using the HAM10000 dataset. To establish the significance of the developed algorithm, the performance parameters of the DCNN-MSCCA are compared with a few existing significant algorithms. The maximum accuracy of DCNN-MSCCA in predicting the exact multi-class skin cancer is 95.1%. This book chapter explains the implementation details of DCNN-MSCCA using python and libraries supporting CNN. 2024 River Publishers. Creator An entity primarily responsible for making the resource Siva Balan R.V.; Senthilnathan T.; Jayapriya J.; Balakrishnan C. Source A related resource from which the described resource is derived Predictive Data Modelling for Biomedical Data and Imaging, pp. 205-230. Publisher An entity responsible for making the resource available River Publishers 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="" target="_blank" rel="noreferrer noopener"></a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196236418&amp;partnerID=40&amp;md5=510b8a08acec16ed10d3e6c5b4a69ba9" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196236418&amp;partnerID=40&amp;md5=510b8a08acec16ed10d3e6c5b4a69ba9</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-877004076-1; 978-877004077-8 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 Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Siva Balan R.V., Department of Computer Science, Christ (Deemed to be University), India; Senthilnathan T., Department of Computer Science, Christ (Deemed to be University), India; Jayapriya J., Department of Computer Science, Christ (Deemed to be University), India; Balakrishnan C., Department of Computer Science, Christ (Deemed to be University), India