Enhanced Vitiligo Classification Using Firefly-Optimized DenseNet201: A Comparative Study of State-of-the-Art Transfer Learning Models

The study examines in detail how deep learning models (MobileNet, ResNet50, EfficientNetB4, InceptionV3, Xception, and DenseNet201) support the automated detection of vitiligo in medical images. Further, this research also seeks to design a solid transfer learning algorithm to get optimum results by joining DenseNet and Firefly Optimization, which is known as firefly-optimized DenseNet201and it efficiently deals with classifying a range of images from the dataset. The dataset was obtained from Kaggle, which is based on two classes, i.e., vitiligo and healthy. Training the model on 3,628 skin images was accomplished, where the accuracy and loss were reviewed on the training and validation sets as each epoch finished. MobileNet reached high training accuracy quickly (93.86%), but it started overfitting after epoch 4. ResNet50 on the other hand, displayed steady validation accuracy of 87.4%. Using bio-inspired methods to tune hyperparameters, the firefly-optimized DenseNet201 achieved both high training accuracy (97.77%) and validation accuracy (89.58. This model may be useful as a computer-aided tool for dermatologists, particularly in areas where there are shortages and prompt and precise vitiligo detection is crucial. The findings suggest useful ways to use deep learning in dermatology by focusing on both efficient processing and correct results.