Minimal sourced and lightweight federated transfer learning models for skin cancer detection - Vikas Khullar; Prabhjot Kaur; Shubham Gargrish; Anand Muni Mishra; Prabhishek Singh; Manoj Diwakar; Anchit Bijalwan; Indrajeet Gupta (Scientific Reports)
Abstract One of the most fatal diseases that affect people is skin cancer. Because nevus and melanoma lesions are so similar and there is a high likelihood of false negative diagnoses challenges in hospitals. The aim of this paper is to propose and develop a technique to classify type of skin cancer with high accuracy using minimal resources and lightweight federated transfer learning models. Here minimal resource based pre-trained deep learning models including EfficientNetV2S, EfficientNetB3, ResNet50, and NasNetMobile have been used to apply transfer learning on data of shape $$\:\:224×\:224×\:3$$ . To compare with applied minimal resource transfer learning, same methodology has been applied using best identified model i.e. EfficientNetV2S for images of shape $$\:\:32×\:32×\:3$$ . The identified minimal and lightweight resource based EfficientNetV2S with images of shape $$\:32×\:32×\:3$$ have been applied for federated learning ecosystem. Both, identically and non-identically distributed datasets of shape $$\:32×\:32×\:3$$ have been applied and analyzed through federated learning implementations. The results have been analyzed to show the impact of low-pixel images with non-identical distributions over clients using parameters such as accuracy, precision, recall and categorical losses. The classification of skin cancer shows an accuracy of IID 89.83% and Non-IID 90.64%.