Improved Segmentation Model for Melanoma Lesion Detection Using Normalized Cross-Correlation-Based k-Means Clustering

The most unexpected and fatal kind of cancer, melanoma, has been on the rise in its spread to different parts of the body. Early detection can significantly reduce its fatality rate. The primary stages of melanoma for its identification by the unaided eye are a difficult task that demands extensive training and understanding. Due to a lack of qualified dermatologists, a computerized and automated method is required to correctly detect melanoma. This study achieved this feat through a proposed system that can effectively detect and classify melanoma as benign or malignant. The process begins with image template matching by using normalized cross-correlation technique induction to mark the infected area of skin lesion as the region of interest (ROI) from ISIC datasets 2017, 2019, and 2020 dermoscopic images. Our novel model dynamically calculated number of clusters is assigned to the
$k$
-means clustering algorithm, and ROI is extracted. Histogram equalization is applied to the output image for contrast enhancement. Hu Moment method is implemented for shape classification and part recognition from the segmented image. GLCM-based Haralick feature extractor is used in the proposed system to extract textural features generating the feature vector from the segmented skin lesion. It leads the classifier to identify skin lesions as cancerous or non-cancerous. Random Forest, Decision Tree, Support Vector Machine (SVM), Gaussian Naïve Bayes, Logistic Regression, and K-nearest neighbor (KNN) Classifiers are used for classification. The KNN, SVM, and Random Forest was successful in attaining the highest accuracy of 99.29%, 99.38%, and 99.46% on the given dermoscopic images datasets ISIC-2019, ISIC-2020 and ISIC-2017 respectively. The proposed method, the normalized cross correlation-based k-means clustering model, was found to be more robust and accurate than existing methods and incorporates much more feature information from the images.