Enhancing Skin Lesion Classification using Deep Learning Features and Genetic Algorithm-Optimized Cosine Weighted KNN

The effective care of skin cancer relies on the fine detection of skin lesions. Deep learning techniques are increasingly being used in medical diagnosis, ranging from the classification of skin lesions. Their ability to learn deep discriminative features from dermoscopic images is what makes them popular. In spite of the fact that deep learning approaches learn rich semantically rich information, the approaches currently being taken tend to suffer from poor generalization, high levels of redundancy, and KNN classifiers that assign identical weights to all neighbors. The paper proposes a new approach using machine learning for the classification of skin lesions, entailing deep feature extraction, techniques for dimensionality reduction, and approaches for optimization. Specifically, the ResNet50 architecture using Global Average Pooling for deep feature extraction from dermoscopic images will be employed. The most relevant and non-redundant features are identified through the Minimum Redundancy Maximum Relevance (mRMR) method. mRMR removes irrelevant class information and reduces the feature size considerably. A new approach for the KNN classifier substitutes the fully connected layer of ResNet50. The weights for instance and feature levels are computed with the Genetic Algorithm (GA) and the use of cosine similarity. The proposed approach attains a high accuracy of 90.61% on the classification task for the binary images of skin lesions. The experimental results show that the proposed optimized cosine weighted KNN approach is effective for the diagnosis of skin cancer.