Feature Selection Based on Dragonfly Optimization for Psoriasis Classification - D. Venkata Sekhar, M. Purushotham Reddy, N. Bhaswanth (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Due to their efficiency and higher disease detection accuracy than traditional methods, metaheuristic algorithms are prominent in healthcare data analysis. The Dragonfly Algorithm (DFA) uses wrapper feature selection to find illness categorization features. DFA was used to pick features and recognize skin illnesses using CNN, VGG19, and EfficientNet-B2 classifiers. The classifier’s accuracy using a given set of features from the training dataset determined the Dragonflies’ fitness value in each iteration. The experimental study showed DFA’s precision and little loss. Two EfficientNet-B2 and VGG19-based CNN models were created in tandem to analyze performance. DermNet NZ and ISIC 2019 were used to train these models. Disease taxonomy helped the models classify. Both datasets classified all eight skin illnesses with an average accuracy of 88.5% and 0.0003 loss. This shows that Deep Learning can classify a wide range of skin conditions with near-human accuracy and reproducibility. These models can also help clinicians perform large-scale screenings utilizing clinical or dermoscopic images for real-time skin disease diagnosis, improving healthcare practices and patient outcomes. This work advances skin disease diagnostics and shows the medical potential of Deep learning.