Document Type : Original Article

Author

• Maryam Nazari

Abstract

Skin cancer is one of the deadliest but most prevalent types of cancer; as such, early diagnosis is urgently required to improve patient outcomes. This work presents a collaborative deep learning model that classifies skin cancer with respect to three different networks: EfficientnetB1, EfficientnetB2, and EfficientnetV2s on dermoscopic images. The proposed collaborative model has a multi-head attention mechanism, ensuring that this model has a better attention capability for improving its accuracy in the task of classification. The HAM10k dataset provided the proposed model with a platform for fine tuning with transfer learning, along with some augmentation techniques to handle class imbalance challenges and feature variations of lesions. The results for the ensemble model combined with Multi-Head Attention were very high: an accuracy of 97.11%, and precision, recall, and F1-score are also very high. These findings prove that our approach can dramatically improve automation in skin cancer detection. Therefore, it will be helpful in clinical dermatology for early diagnosis in medicine.

Keywords

• Skin lesion classification
• Multi-head attention
• Ensemble learning
• Medical imaging

Main Subjects

• Artificial Intelligence, Robotics, Cognitive Computing