D2LFS2Net: Multi‐class skin lesion diagnosis using deep learning and variance‐controlled Marine Predator optimisation: An application for precision medicine
Abstract In computer vision applications like surveillance and remote sensing, to mention a few, deep learning has had considerable success. Medical imaging still faces a number of difficulties, including intra‐class similarity, a scarcity of training data, and poor contrast skin lesions, notably in...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-02-01
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| Series: | CAAI Transactions on Intelligence Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cit2.12267 |
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| Summary: | Abstract In computer vision applications like surveillance and remote sensing, to mention a few, deep learning has had considerable success. Medical imaging still faces a number of difficulties, including intra‐class similarity, a scarcity of training data, and poor contrast skin lesions, notably in the case of skin cancer. An optimisation‐aided deep learning‐based system is proposed for accurate multi‐class 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‐trained deep learning models, MobileNetV2 and NasNet Mobile, are trained using deep transfer learning on the upgraded enriched dataset. Later, a dual‐threshold serial approach is employed to obtain and combine the features of both models. The next step was the variance‐controlled 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. |
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| ISSN: | 2468-2322 |