A weighted ensemble transfer learning approach for melanoma classification from skin lesion images

Cancer is the foremost cause of mortality among humans, as per statistics and accurate classification of the lesion is critical for treating skin cancer at an early stage. Identification of the disease via computer-aided tools can help in accurate diagnosis. This study’s primary goal is to suggest an effective strategy for more accurately classifying skin lesions. The binary classification of skin lesions has been proposed using the weighted average ensemble approach. The predictions from various models are combined via the weighted sum ensemble, where the weights of each model are determined by how well it performs. Weights for each learner in the weighted ensemble are scientifically determined based on their average accuracy on the testing dataset. The proposed weighted ensemble classifier uses an ensemble of seven deep-learning neural networks to perform binary classification, including InceptionV3, VGG16, Xception, ResNet50, and others. The International Skin Imaging Collaboration (ISIC) dataset has been used for experimentation, which is binary classified into Melanoma and Nevus. The proposed ensemble method provides the highest level of accuracy, precision, recall, f1-score, sensitivity, and specificity of 93.36%, 93%, 93%, 93%, 97%, and 97% respectively on the first ISIC dataset. The proposed methodology’s efficiency has also been compared and evaluated with another ISIC dataset. On the other ISIC dataset, the proposed weighted ensemble classifier had an accuracy of 85.54%. Additionally, the proposed methodology has been compared with state-of-art techniques. a weighted ensemble method where the final result decision is made based on the weighted total of the anticipated outputs from the classifiers. Each model is given a specific weight, which is then multiplied by the value it predicted and used to get the sum or average forecast. The suggested classification model concluded about the expected probabilities for each class and selected the class with the highest probability.