Federated Learning Assisted Deep learning methods fostered Skin Cancer Detection: A Survey

Abstract

Skin Cancer (SC) is a significant problem for public health on a global scale. Its early identification is essential for improving patient prognostics. However, there are substantial problems in this field with regard to the dearth of trained specialists and medical equipment. Deep learning-based approaches have significantly improved Skin Cancer Detection (SCD) as compared to traditional Machine Learning (ML) tasks and have attained high performance. Deep learning (DL) methods used for automated SCD have been popular in this domain. Several DL techniques have been put forth as of late to accomplish Federated Learning (FL) based SCD. There are several steps in the SCD employing deep learning and the FL model. Initially, primary sources and standardised databases are used to gather images of SC from a variety of patients. The next step is data cleaning, which includes noise reduction, resizing, and contrast enhancement. Additionally, the affected malignant section is segmented using edge-based, region-based, and morphological-based segmentation techniques. Following the extraction of features from the photos, deep learning approaches with FL assistance are used to classify the images. Last but not least, the FL-aided deep learning techniques categorise the image as malignant and non-cancerous. This review, which was undertaken by pulling data from 100 papers published between the years 2019 and 2023, provides a thorough statistical analysis. Finally, this survey will be beneficial for SCD researchers.