Deep Learning-Based Classification of Skin Lesions: Enhancing Melanoma Detection through Automated Preprocessing and Data Augmentation

Abstract

Skin cancer of the most dangerous type, melanoma, requires an early and accurate diagnosis for its treatment to reduce mortality and increase the number of positive outcomes. Even with the availability of better imaging and diagnostic techniques, it is still difficult to differentiate between benign lesions and malignant melanoma because of overlapping features, noisy images and images with artefacts such as hair and glare. To overcome these challenges, this research adopts deep learning models to classify skin lesions based on images from the ISIC Archive dataset. The study establishes a strong two-stage classification framework. Therefore, noise reduction, ROI cutting, and data enhancement techniques are used for data pre-processing. Second, lesion classification is performed using a ResNet-based convolutional neural network (CNN) architecture. The model is trained and validated on a balanced dataset that contains an equal number of benign and malignant lesion categories. Using accuracy, precision, recall, F1 score and AUC, the system can be assessed and compared to other state-of-art approaches. The findings show that the proposed model has a high level of classification performance and a high level of discriminative ability between melanoma and benign lesions. The ROC curve effectively exhibits the model’s performance and accuracy, and the confusion matrix reveals tendencies to misclassify and where it should be improved. The application of sophisticated preprocessing methods improves model performance, responding to the issues arising from the presence of noise in data. This research is valuable to the field of dermatological diagnostics as it offers a scalable, automated means of skin lesion classification. The proposed framework can be applied clinically to assist dermatologists in the detection of early melanoma and, therefore benefit patients. Subsequent studies will address the development of combined approaches and the improvement of interpretability aids in order to increase the diagnostic accuracy and practical applicability of the methods in clinical practice.