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Medical imaging still faces a number of difficulties, including intra\u2010class similarity, a scarcity of training data, and poor contrast skin lesions, notably in the case of skin cancer. An optimisation\u2010aided deep learning\u2010based system is proposed for accurate multi\u2010class skin lesion identification. The sequential procedures of the proposed system start with preprocessing and end with categorisation. The preprocessing step is where a hybrid contrast enhancement technique is initially proposed for lesion identification with healthy regions. Instead of flipping and rotating data, the outputs from the middle phases of the hybrid enhanced technique are employed for data augmentation in the next step. Next, two pre\u2010trained deep learning models, MobileNetV2 and NasNet Mobile, are trained using deep transfer learning on the upgraded enriched dataset. Later, a dual\u2010threshold serial approach is employed to obtain and combine the features of both models. The next step was the variance\u2010controlled Marine Predator methodology, which the authors proposed as a superior optimisation method. The top features from the fused feature vector are classified using machine learning classifiers. The experimental strategy provided enhanced accuracy of 94.4% using the publicly available dataset HAM10000. Additionally, the proposed framework is evaluated compared to current approaches, with remarkable results.<\/jats:p>","DOI":"10.1049\/cit2.12267","type":"journal-article","created":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T06:39:36Z","timestamp":1693377576000},"page":"207-222","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["D2LFS2Net: Multi\u2010class skin lesion diagnosis using deep learning and variance\u2010controlled Marine Predator optimisation: An application for precision medicine"],"prefix":"10.1049","volume":"10","author":[{"given":"Veena","family":"Dillshad","sequence":"first","affiliation":[{"name":"Department of Computer Science HITEC University  Taxila Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5723-3858","authenticated-orcid":false,"given":"Muhammad Attique","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Computer Science HITEC University  Taxila Pakistan"},{"name":"Department of Computer Science and Mathematics Lebanese American University  Beirut Lebanon"}]},{"given":"Muhammad","family":"Nazir","sequence":"additional","affiliation":[{"name":"Department of Computer Science HITEC University  Taxila Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9520-3174","authenticated-orcid":false,"given":"Oumaima","family":"Saidani","sequence":"additional","affiliation":[{"name":"Department of Information Systems, College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. 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