A Novel Hybrid Deep Learning Network for Skin Cancer Detection From Skin Images

Skin disease is a disorder that damages the skin by causing irritation, inflammation, or abnormal growth. In the olden days, there were limited diagnostic tools, which made it difficult to identify and treat the disease accurately. Therefore, a novel Convolutional Dense Network (Conv-DenNet) is designed for the detection of skin cancer. The Conv-DenNet model combines Convolutional eXtreme Gradient Boosting (ConvXGB) and Dense Network (DenseNet). Initially, the input skin image is denoised using the Medav filter. Following this, skin lesion segmentation is performed via the Recurrent Residual U-Net integrated with TopK Focal loss (TKF + R2U-Net). Here, the R2U-Net model is fine-tuned via Artificial Namib Beetle Optimization (ANBO), which integrates the Artificial Hummingbird Algorithm (AHA) with Namib Beetle Optimization (NBO). Moreover, image augmentation is applied using a combination of geometric and color space transformations, along with Mixup and CutMix. Following this, the images are analyzed to extract statistical and Haralick features. Finally, skin cancer detection is performed using the Conv-DenNet model. Experimental results show that Conv-DenNet achieves 93.46% accuracy, 93.78% True Positive Rate (TPR), 94.37% precision and 6.38% False Positive Rate (FPR), under 5 leave-one-out cross-validation. The study shows that the Conv-DenNet model improves the detection of skin cancer relative to the conventional methods.