Deep Learning and Crow Search to Detect Skin Cancer in Dermoscopic Images

The accurate and timely diagnosis of skin cancer is crucial for reducing the associated mortality and morbidity rates, given the prevalence of this severe condition. Dermoscopic pictures provide doctors and researchers a closer look at skin lesions. This research uses cutting-edge image analysis and machine learning (ML) methods to provide a framework for dermatoscopic skin cancer analysis. We present the Crow Search Algorithm with Deep Learning for Skin Cancer Diagnoses on Dermoscopy Images (CSADL-SCDDI) model in this study. Bilateral filter (BF) preprocessing improves dermatoscopic pictures in the model. This preprocessing phase prepares pictures for analysis. For feature extraction, SqueezeNet is used. SqueezeNet is ideal for extracting key visual information due to its small, efficient design. SqueezeNet extracts complete characteristics for reliable skin cancer diagnosis. Model categorization uses Recurrent Neural Networks. RNNs excel at sequential data processing and feature set temporal relationships. RNNs can track skin lesion evolution, making them ideal for identifying benign from malignant skin cancer. The Crow Search Algorithm (CSA) optimizes hyper-parameters to improve model performance. CSA is inspired by crows' sophisticated foraging and used to fine-tune RNN parameters. This enhancement boosts system diagnostic accuracy. The system is tested on a large dataset of dermatoscopic pictures to prove its effectiveness. The framework's robustness and reproducibility in skin cancer analysis suggest it might greatly enhance diagnostic techniques.