Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, 44519, Egypt; Faculty of Computer Science and Engineering, Galala University, Suez 435611, Egypt; Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates; Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon; MEU Research Unit, Middle East University, Amman 11831, Jordan.
Mathematics and Computer Science Department, University of Ahmed DRAIA, 01000, Adrar, Algeria.
Comput Biol Med. 2023 Sep;163:107154. doi: 10.1016/j.compbiomed.2023.107154. Epub 2023 Jun 19.
Accurate skin lesion diagnosis is critical for the early detection of melanoma. However, the existing approaches are unable to attain substantial levels of accuracy. Recently, pre-trained Deep Learning (DL) models have been applied to tackle and improve efficiency on tasks such as skin cancer detection instead of training models from scratch. Therefore, we develop a robust model for skin cancer detection with a DL-based model as a feature extraction backbone, which is achieved using MobileNetV3 architecture. In addition, a novel algorithm called the Improved Artificial Rabbits Optimizer (IARO) is introduced, which uses the Gaussian mutation and crossover operator to ignore the unimportant features from those features extracted using MobileNetV3. The PH2, ISIC-2016, and HAM10000 datasets are used to validate the developed approach's efficiency. The empirical results show that the developed approach yields outstanding accuracy results of 87.17% on the ISIC-2016 dataset, 96.79% on the PH2 dataset, and 88.71 % on the HAM10000 dataset. Experiments show that the IARO can significantly improve the prediction of skin cancer.
准确的皮肤损伤诊断对于早期发现黑色素瘤至关重要。然而，现有的方法无法达到较高的准确性。最近，预训练的深度学习（DL）模型已被应用于解决和提高皮肤癌检测等任务的效率，而不是从头开始训练模型。因此，我们开发了一种基于 DL 的模型的皮肤癌检测的稳健模型，该模型使用 MobileNetV3 架构作为特征提取骨干。此外，引入了一种名为改进人工兔子优化器（IARO）的新算法，该算法使用高斯突变和交叉算子来忽略从 MobileNetV3 提取的特征中的不重要特征。PH2、ISIC-2016 和 HAM10000 数据集用于验证所开发方法的效率。实验结果表明，所开发的方法在 ISIC-2016 数据集上的准确率达到了 87.17%，在 PH2 数据集上的准确率达到了 96.79%，在 HAM10000 数据集上的准确率达到了 88.71%。实验表明，IARO 可以显著提高皮肤癌的预测能力。
