Deep Learning-Based Classification of Melanoma Using Dermoscopic Images: Comparative Evaluation of Convolutional Neural Network and DenseNet Models

For melanoma, an aggressive form of skin cancer, early and accurate detection is critical to improving clinical outcomes. Deep learning-based image analysis has emerged as a promising approach for computer-assisted evaluation of dermoscopic lesions. However, model performance is often affected by limited dataset size, class imbalance, overfitting, and differences in network architecture and optimization strategies.

This study was aimed at developing and comparatively evaluating convolutional neural network (CNN) and DenseNet-based deep learning models for automated classification of dermoscopic skin lesion images. The effects of data augmentation, network architecture, optimizer selection, and training duration on classification performance were examined.

Publicly available dermoscopic skin lesion images obtained from Kaggle were evaluated using a conventional CNN, DenseNet169, and DenseNet201. The original dataset contained 2357 images representing multiple benign and malignant skin lesion categories. Images were resized to 250×250 pixels, labeled, and randomly partitioned into training and testing sets at an 80:20 ratio. To address class imbalance, image augmentation using transformations, including ±10° shear, was performed, increasing the dataset size to 6857 images, with approximately 500 additional samples generated per class. Models were trained using adaptive moment estimation (Adam) and root mean square propagation (RMSprop) optimizers, and evaluated after 20, 60, and 100 epochs. Performance was assessed using accuracy, precision, recall (sensitivity), specificity, F₁-score, confusion matrices, and training and validation loss.

Data augmentation substantially improved model performance and reduced evidence of overfitting compared with the original dataset. On the augmented dataset, DenseNet169 and DenseNet201 achieved accuracy and F₁-scores exceeding 94% after 100 epochs, consistently outperforming the conventional CNN. DenseNet169 achieved up to 95% validation accuracy, with only minor performance differences between the Adam and RMSprop optimizers. The CNN showed greater susceptibility to overfitting on the smaller dataset; its F₁-score, calculated over the complete dataset, increased from 31% after 20 epochs to 59% after 60 epochs and 87% after 100 epochs before augmentation. Across the comparative experiments, the DenseNet architectures demonstrated more stable validation performance and better generalization following augmentation.

DenseNet169 and DenseNet201 demonstrated stronger and more stable classification performance than the conventional CNN for dermoscopic skin lesion classification, particularly after augmentation of the training dataset. Increasing the dataset size and improving class balance led to reduced overfitting and improved model performance. These findings support further investigation of DenseNet-based approaches as potential clinical decision support tools for melanoma and skin lesion assessment. However, prospective evaluation using independent, multicenter datasets and diverse patient populations is required before clinical implementation.