An Effective Approach to Detect Melanoma Using Semantic Mathematical Model and Modified Golden Jackal Optimization Algorithm

Abstract

Melanoma skin cancer is the most life-threatening and fatal disease in the family of skin cancer diseases. Detecting melanoma at its early stage can improvise survival which requires an effective classification technique to categorize dermoscopic images as melanoma and non-melanoma. Based on the quality of the extracted features, the classification accuracy of the classifier is determined. However, the classification accuracy of the existing approaches is poor due to the presence of improper image boundaries and low quality. To overcome the fore-mentioned issues, this research introduced an optimization-based feature selection approach using the modified golden jackal optimization (MGJO) algorithm. The pre-processed image is segmented using a semantic mathematical model known as saliency-based level set with improved boundary indicator function (SLSIBIF) and the feature extraction is performed using the GoogleNet architecture. After this, the proposed MGJO algorithm was used to select the relevant features which aid in precise classification performed using multiclass-support vector machine (MSVM). The obtained results show that the proposed MGJO-MSVM achieves enhanced classification accuracy of 98.89 % for the ISIC-2017 dataset whereas the accuracy of the existing feature adaptive transformer network (FAT-NET), multi-attention fusion convolutional neural network-based skin cancer diagnosis (MAFCNN-SCD), W-net inception residual network, region-based convolutional neural network with fuzzy k-means clustering (RCNN-FKM) and gated fusion attention network is 93.26%, 92.22%, 96.97%, 95.6% and 93.97% respectively.