Integrating Principal Component Analysis and Multi-Input Convolutional Neural Networks for Advanced Skin Lesion Cancer Classification

Abstract

The importance of early detection in the management of skin lesions, such as skin cancer, cannot be overstated due to its critical role in enhancing treatment outcomes. This study presents an innovative multi-input model that fuses image and tabular data to improve the accuracy of diagnoses. The model incorporates a dual-input architecture, combining a ResNet-152 for image processing with a multilayer perceptron (MLP) for tabular data analysis. To optimize the handling of tabular data, Principal Component Analysis (PCA) is employed to reduce dimensionality, facilitating more focused and efficient model training. The model’s effectiveness is confirmed through rigorous testing, yielding impressive metrics with an F1 score of 98.91%, a recall of 99.19%, and a precision of 98.76%. These results underscore the potential of combining multiple data inputs to provide a nuanced analysis that outperforms single-modality approaches in skin lesion diagnostics.

Keywords

• skin cancer detection
• multi-input deep learning
• Principal Component Analysis (PCA)
• medical image augmentation