Predicting Breast Cancer Relapse Images Employing Integrated Machine Learning and Deep Transfer Learning Approaches
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Abstract
Breast cancer is a significant contributor to the increasing global mortality rate, especially among women. The problem of delayed cancer diagnosis is a major concern as it leads to reduced effectiveness of treatment and higher mortality rates. This research aims to create a predictive model for breast cancer relapse. Various machine learning methods were used to achieve accurate predictions of cancer relapse. In order to achieve enhanced predictive outcomes in the case of breast cancer relapse images, the hybrid approach of machine learning approaches, including complex decision tree, quadratic support vector machine, Gaussian medium support vector machine, ensemble subspace k nearest neighbor, and extreme learning machine, are used as classifiers and deep transfer learning approaches including Alexnet, GoogleNet, EfficientNet, VGG-19, and ResNet-18 are used as feature extraction techniques, is proposed. The proposed hybrid approaches were evaluated using various performance metrics. It was found that the ELM classifier applied to the featured extracted dataset is the most suitable for predicting cancer relapse with enhanced achievements with 97.08% accuracy, 97.41% precision, 98.26% sensitivity, 94.64% specificity, 97.83% f-score, 96.45% balanced accuracy and 0.986 AUC. This proposed hybrid approach has the potential to improve cancer relapse prediction.
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