SVM-Based Skin Cancer Diagnosis for Malignant and Benign Tumor Distinction

In this exploration, we have successfully developed an SVM model that can enhance the process of skin cancer diagnosis. By utilizing advanced techniques in image analysis such as SVM, we have extracted unique features from dermatoscopic images of skin cancer, enabling us to detect distinctive dermoscopy patterns that indicate benign or malignant cancer tumors. The work aims to distinguish between benign and malignant growths, and we improved the accuracy of our diagnostic approach by using a support vector classifier and its capabilities. The work comprises the combination of dermoscopic image classification, machine learning, and, medical imaging, which together form a diversified diagnostic tool from various fields. After conducting attentive testing on different datasets to ensure the performance of our SVM model and the ease of use, satisfied outcomes occurred. The objective is to create a model i.e., a diagnostic tool to distinguish dermoscopic images and improve the early detection technology in skin cancer diagnosis to improve skin health of patients and raise awareness among the precautions to be taken.