| [22] | The preprocessing images and fnetuning convolutional neural networks with transfer learning, with EffcientNet B4 identifed as the top-performing model. | HAM10000 dataset | F1 Score: 87%, Accuracy: 87.91% |
| [23] | Automated Skin-Melanoma Detection (ASMD) system using image processing and SVM-based classifcation, proposing a Melanoma-Index (MI) for clinical use. | DD image dataset | Accuracy: 97.50% |
| [24] | Automatic skin cancer diagnosis system including Histogram of Gradients (HG) and Histogram of Lines (HL), combined with other features. | HPH dermoscopy database and the Dermoft standard database | Accuracy: 98.79% (HPH) and 92.96% (the standardDermoft) |
| [25] | Skin cancer detection system utilizing Genetic Programming (GP) for evolving a classifer and feature selection. | PH2dataset | Accuracy: 97.92% |
| [26] | Image processing and deep learning techniques, including Convolutional Neural Networks (CNNs), for skin cancer detection and classifcation. | MNISTHAM10000 dataset | Weighted Average Accuracy: 0.88, WeightedAverage Recall: 0.74, Weighted F1-score: 0.77 |
| [27] | Classifcation of skin lesions, utilizing dynamic-sized kernels and both ReLU and leakyReLU activation functions. | HAM10000 dataset | Overall accuracy: 97.85% |
| [28] | Soft-Attention mechanism in deep neural architectures for skin lesion classifcation. | HAM10000 dataset and ISIC-2017 dataset | Precision: 93.7% (HAM10000), sensitivity: 91.6% (ISIC-2017) |
| [29] | MobileNetV3 introducing the Improved Artifcial Rabbits Optimizer (IARO) algorithm to enhance feature selection | PH2, ISIC-2016, and HAM10000 datasets | Accuracy: 87.17% (ISIC-2016), 96.79% (PH2 dataset), and 88.71% (HAM10000) |
| [30] | SkinTrans, an improved transformer network, for skin cancer classifcation, utilizing vision transformers (VIT) with self-attention mechanism. | HAM10000 and clinical datasets | Accuracy: 94.3% (HAM10000) and 94.1% (Clinical) |
