Hybrid deep learning technique for optimal segmentation and classification of multi-class skin cancer

ABSTRACTThis study introduces a novel deep learning-based approach for skin cancer diagnosis and treatment planning to overcome existing limitations. The proposed system employs a series of innovative algorithms, including IQQO for preprocessing, TSSO for cancer region isolation, and FA-MFC for data dimensionality reduction. The USSL-Net DCNN extracts hidden features, and the BGR-QNN enables multi-class classification. Evaluated on Kaggle and ISIC-2019 datasets, the model achieves impressive accuracy, up to 96.458% for Kaggle and 94.238% for ISIC-2019. This hybrid deep learning technique shows great potential for improving skin cancer classification, thus enhancing diagnosis and treatment outcomes and ultimately reducing mortality rates.KEYWORDS: Skin cancerhybrid deep learningpreprocessingsegmentationfeature extractionfeature optimizationmulti-classFA-MFC technique Disclosure statementNo potential conflict of interest was reported by the authors.Data availability statement:Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.Additional informationNotes on contributorsG. SubhashiniG. Subhashini obtained her Bachelor's degree in computer science and engineering from Dhanalakshmi College of Engineering, Anna University in 2012 and her Master's degree in computer science and engineering from Sriram Engineering College, Anna University in 2014. She is currently pursuing her Ph.D. degree at Anna University and working as an assistant professor at St. Joseph's Institute of Technology, Chennai. Her research interest includes Deep Learning, Machine Learning, Natural Language Processing and Cloud computing.A. ChandrasekarDr. A. Chandrasekar (Arumugam Chandrasekar) received his bachelor of engineering degree in computer science and engineering from Angala Amman College of Engineering and Technology, Bharathidasan University in 1998, the Master of Engineering in computer science and engineering degree from Arulmigu Kalasalingam College of Engineering, Madurai Kamaraj University in 2000 and a Ph.D. degree from Anna University, Chennai in 2010. He is currently working as a professor in the department of computer science and engineering at St Joseph's College of Engineering, Chennai. His current research interests are Network Security, Cloud Security, Data mining, Artificial Intelligence and Big Data Analysis. He is a member of IEEE, a life member in NCSSSASSOC, ISTE, CSI, ICSES, CRSI, IAENG, CSES, IACSIT and a Fellow Member of IEI, ISCAI and ISRD.