Deep Learning Approach in Melanoma Stage Classification

Accurate and efficient classification of melanoma stages is crucial for effective treatment planning and improved patient outcomes. Traditional diagnostic methods are often time-consuming and subjective, highlighting the need for advanced computational approaches. This study proposes a self-supervised learning framework combined with a convolutional neural network (CNN) to classify melanoma stages more effectively. Initially, features are extracted from unlabeled skin images using pre-trained VGG16 and ResNet50 models. These features are combined and reduced in dimensionality using Principal Component Analysis (PCA). Subsequently, K-means and DBSCAN clustering is applied to pseudo-label the data, providing a foundation for pre-training a CNN model. This pre-trained model is then fine-tuned on a smaller, labelled dataset to enhance classification accuracy. The methodology shows promising results, validated by significant metrics like Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index. Experimental results confirm the superior efficacy of this approach, enhancing melanoma diagnosis and treatment planning through advanced deep learning techniques.