2-HDCNN: A two-tier hybrid dual convolution neural network feature fusion approach for diagnosing malignant melanoma. (January 2023) - Nancy Jane, Y.; Charanya, S.K.; Amsaprabhaa, M.; Jayashanker, Preetiha; Nehemiah H., Khanna
Abstract: Melanoma is a fatal form of skin cancer, which causes excess skin cell growth in the body. The objective of this work is to develop a two-tier hybrid dual convolution neural network (2-HDCNN) feature fusion approach for malignant melanoma prediction. The first-tier baseline Convolutional Neural Network (CNN) extracts the hard to classify samples based on the confidence factor (class probability variance score) and generates a Baseline Segregated Dataset (BSD). The BSD is then preprocessed using hair removal and data augmentation techniques. The preprocessed BSD is trained with the second-tier CNN that yields the bottleneck features. These features are then combined with the derived features from the ABCD (Asymmetry, Border, Color and Diameter) medical rule to improve classification accuracy. The generated hybrid fused features are fed to different classifiers like Gradient boosting classifiers, Bagging classifiers, XGBoost classifiers, Decision trees, Support Vector Machine, Logistic regression and Multi-layer perceptron. For performance assessment, the proposed framework is trained on the ISIC 2018 dataset. The experimental results prove that the presented 2-HDCNN feature fusion approach has reached an accuracy of 92.15%, precision of 96.96%, specificity of 96.8%, sensitivity of 86.48%, and AUC (Area Under Curve) value of 0.96 for diagnosing malignant melanoma. Highlights: Develop 2-HDCNN feature selection framework for malignant melanoma prediction. 2-HDCNN combines the dual CNN and ABCD attributes for hybrid feature selection. Classification model is built using selected hybrid features. Experimental results have proven the efficiency of the proposed 2-HDCNN approach.