A Hybrid Transfer Learning Framework for Dermatological Diagnostics and Optimized Lesion Analysis using DenseNet121 and Capsule Networks

The potential of deep learning is unlocked by the Capsule Network (CapsNet), considered a novel architectural advancement that is designed to preserve spatial hierarchies which addresses a key limitation of traditional convolution neural networks (CNNs). The project makes use of DenseNet121 which is a powerful model that is pre-trained on ImageNet dataset. By combining DenseNet121 with Capsule Networks and transfer learning, an advanced skin disease detection and personalized care recommendation system is developed. DenseNet121 gives a powerful backbone with robust feature extraction that ensures hight-quality performance in complicated task, while CapsNet improves the accuracy of diagnosis by capturing spatial relationships. Transfer learning greatly enables generalization among diverse skin conditions which reduces the need for additional datasets. The system is trained on a dataset called Ham10000 to detect several skin disorders. Upon diagnosis, the system gives a detailed explanation of the detected condition, recommends medication and have a chatbot for the enhancement of the user experience. This innovative solution supports the dermatologists in offering accurate diagnoses while extending high-quality dermatological care to underdeveloped areas, providing a transformative tool for global healthcare systems