Advanced Sparse Representation Methods Integrated with Deep Learning for Precise Feature Extraction and Pattern Recognition in Multispectral Skin Cancer Image Analysis

Abstract

Among all the dermatological diseases skin cancer is one of the most common and dangerous ones and where the early and correct diagnosis plays a crucial role in the increase of the patient's survival chances. Nevertheless, the traditional diagnosis methods are painful, long, and heavily reliant on doctors' skills; at the same time, the current deep learning systems struggle with noise and with having the same features repeatedly in the images taken through a dermoscope. To overcome the above-mentioned difficulties the authors of this paper present a hybrid deep learning framework that is aware of the noise and merges Non-Local Means (NLM) denoising, sparse dictionary learning, and a ResNet-152V2 deep residual network for accurate multispectral skin lesion classification. The NLM preprocessing step successfully removes Gaussian and impulse noise but still retaining the delicate lesions, thus, improving the image quality. Next, sparse dictionary learning transforms the noise free images into compact and discriminative representation which leads to less redundancy in features and thus more efficient learning. These enhanced features are then classified by the ResNet-152V2 architecture, which makes use of deep residual connections to not only capture the complex hierarchical patterns but also to guarantee stable training. The proposed method which is supported by the mechanical and manual testing performed on the HAM10000 and ISIC-2019 benchmark datasets not only surpasses conventional CNN architectures like VGG16, ResNet50, DenseNet121, and InceptionV3 but also achieves 85.61% accuracy with an AUC of 0.977 on HAM10000 and 81.23% accuracy with an AUC of 0.963 on ISIC-2019. The results endorse the synergistic integration of noise-resistant preprocessing, sparse feature representation, and deep residual learning to be the key factors that drive the performance of the system in terms of classification accuracy, robustness, and cross-dataset generalization. To summarize, the proposed method is a friendly tool in the battle against skin cancer. It being scalable, non-invasive, and clinically applicable makes it a strong candidate for real-world dermatological and telemedicine application deployment.