A Computing Framework for Transfer Learning and Ensemble Classification of Surface Patterns

The rapid increase in population density has posed significant challenges to medical sciences in the auto-detection of various diseases. Intelligent systems play a crucial role in assisting medical professionals with early disease detection and providing consistent treatment, ultimately reducing mortality rates. Skin-related diseases, particularly those that can become severe if not detected early, require timely identification to expedite diagnosis and improve patient outcomes. This paper proposes a transfer learning-based ensemble deep learning model for diagnosing dermatological conditions at an early stage. Data augmentation techniques were employed to increase the number of samples and create a diverse data pattern within the dataset. The study applied ResNet50, InceptionV3, and DenseNet121 transfer learning models, leading to the development of a weighted and average ensemble model. The system was trained and tested using the International Skin Imaging Collaboration (ISIC) dataset. The proposed ensemble model demonstrated superior performance, achieving 98.5% accuracy, 97.50% Kappa, 97.67% MCC (Matthews Correlation Coefficient), and 98.50% F1 score. The model outperformed existing state-of-the-art models in dermatological disease classification and provides valuable support to dermatologists and medical specialists in early disease detection. Compared to previous research, the proposed model offers high accuracy with lower computational complexity, addressing a significant challenge in the classification of skin-related diseases.