An Efficient Method to Minimize the Depth Estimation Error in Melanoma Skin Cancer Classification
K Amit Kumar, T. Y. Satheesha
Abstract
Melanoma skin cancer is widely propagating cancer in USA. The processes of biological changes are restricted for customized processing and observation. The researchers have proposed various classifications and categorization techniques to validate skin cancer. In this paper, a novel classification and cluster validation technique to minimize the error estimation and image depth validation. The proposed technique has included Convolutional Neural Network (CNN) framework to ensure dataset (2D and 3D images) depth analysis under attribute extraction. The process of dilation residual inceptions assures the overall dataset is computed under convolution feature decomposition. The extracted attributes and schematic representation of decomposed CNN is fetched for depth computation. The technique has successfully processed and validated on Kaggle based melanoma datasets. The technique has secured an accuracy of 95.68% with respect to 60:40 training and testing ratio and an accuracy of 95.16% with 70:30 respectively.