Skin Cancer Classification in Dermoscopic Images Using Multi‐Scale Feature Map Fusion Based on Deep Learning

Skin cancer is a prevalent and potentially life‐threatening condition that requires accurate and timely diagnosis. Dermoscopic imaging aids in diagnosis but is often limited by manual interpretation, which can be time–intensive and error–prone. Automated classification systems using convolutional neural networks (CNNs) have shown significant promise in enhancing accuracy and efficiency. To further improve classification performance, we propose a novel deep learning‐based Multi–Scale Feature Map Fusion (MFMF) model that extracts and fuses features from multiple convolutional layers. The MFMF module effectively combines these multi–scale features, enabling robust feature capture even with limited datasets. Additionally, a hair removal algorithm is proposed to enhance prediction accuracy through improved image preprocessing. On the HAM10000 dataset, our proposed model achieves an overall accuracy of 96.12%, an AUC of 98.7%, and an F1 Score of 93.29%, along with notable improvements in precision, recall, and sensitivity.