Feature Fusion for Improved Skin Cancer Diagnosis Using Support Vector Machines

Abstract

the early detection and successful treatment of skin cancers, a potent form of cancer, calls for the use of sophisticated diagnostic instruments. This study delves into the use of support vector machines (SVMs), to cope with the inconsistencies occurring among skin lesions, by merging them with feature fusion techniques. SVMs are preferred for this situation, as they are highly effective when it comes to the management of exceedingly dimensional data. Initially, in order to train and enhance the diagnostic capacity of the SVM classifier, a single and all-inclusive single dataset was generated through the analysis, identification and extraction of a wide variety of explanatory features (including colour, texture and shape) from a dataset comprising 10000 dermatoscope skin lesion representations. This was followed by the use of early and late fusion approaches, to generate an extensive dataset of descriptions, for assessing the reliability of the SVM classifier. Finally, the accuracy, precision and recall of the SVM classifier were ascertained by way of an objective dataset, comprising 25 dermatoscope representations of malignant and benign lesions. The accuracy, precision and recall of the SVM classifier are supported by its capacity to distinguish 10 true positives, 12 true negatives, three false positives and zero false negatives. As such, the SVM classifier can be considered effective, for the early detection of skin cancers. The results from this investigation verify that the capacity of SVMs, in terms of skin cancer diagnosis, is greatly improved with the utilization of feature fusion techniques. Also verified through this undertaking, is the effectiveness of innovative computational procedures, for the delivery of dependable medical diagnoses.