Leveraging SCNN for Enhanced Skin Cancer Detection Using Deep Learning Approaches - Thilaka, A.; Nisha, V.; Nathiya, T. (Journal of Neonatal Surgery, 2025)
This paper presents the establishment of a skin disease identification system exploiting the deep learning strategies, uniquely called Sequential Convolutional Neural Networks (SCNNs), to automate the detection and classification of various skin conditions based on image data. By applying the SCNN proposed model on a varied collection of skin pictures, the system discover to realize the configurations and features reflective of different skin diseases, including melanoma, eczema, and psoriasis. Through feature extraction and classification, the model can accurately diagnose skin diseases. The proposed model produce a non-invasive and cost-effective approach to skin disease diagnosis, facilitating early intervention and timely treatment. Additionally, a user-friendly interface is created to host the system, allowing users to create accounts, upload skin images, and receive reports containing the identified disease along with preventative and precautionary measures. This implementation of deep learning for skin disease identification has the promising potential to enhance healthcare consequences by granting more efficient and more faultless diagnosis, ultimately improving patient care and quality of life. The proposed model gave the highest accuracy of prediction compare to the existing works.