Ensemble optimization algorithm for the prediction of melanoma skin cancer

Abstract

One of the worst illnesses in the world, melanoma has the potential to spread to many body sites if it is not detected early. Because of this, the use of automated diagnostic tools that may help doctors and even laypeople identify a certain illness has resulted in a huge advancement in the medical field. Create a hybrid method for analyzing suspicious lesions and melanoma skin cancer detection. The current scores' performances are heavily reliant on fine-tuned settings and architectures. Even in machine learning and computer vision studies, dynamic data augmentation studies are limited. This work proposes dynamic training/testing enhancements that can greatly enhance effectiveness of proposals. Traditional search strategies that require training new models for augmentations consume more GPU time than this work's proposed framework. The EOA (ensemble optimization algorithm), which does not require model training for every new augmentation technique, accelerates search. The effectiveness of this technique is evaluated against single and ensemble models using the ISIC dataset. Moreover, Efficient Net, a new, compact network design, serves as the system's backbone. This approach yields greater results, and this research also reveals the sought augmentation strategy that was used, which calls for an exceptional amount of resources. So, to enhance performance, other researchers may make advantage of the augmentation strategies found.

Keywords

• Machine learning
• Ensemble model
• Augmentation
• Skin cancer
• Efficientnet
• Dermoscopic images