Automated Identification of Breast Cancer Type Using Novel Multipath Transfer Learning and Ensemble of Classifier - Salini Sasidharan Nair; Mohan Subaji (IEEE Access, 2024)
Breast cancer, a global health concern, requires innovative diagnostic approaches. The potential of Artificial Intelligence and Machine Learning in breast cancer diagnosis warrants exploration along with conventional methods. Our method partitions breast cancer images into four regions by, employing transfer learning using ResNet50 and VGG16 for feature extraction in each region. The extracted features are consolidated and fed into an Extra Tree Classifier. In addition, an ensemble learning framework combines logistic regression, SVM (Support Vector Machine), Extra Tree Classifier, and Ridge Classifier outputs, harnessing the strengths of each for robust breast cancer image classification. Among the five machine learning classification models (— Extra Tree Classifier, Logistic Regression, Ridge Classifier, SVM, and Voting Classifier) — the goal was to determine the most effective in terms of accuracy. Surprisingly, the Voting Classifier emerged as the top performer, with an impressive accuracy of 96.86% across these carcinoma classes, validating the effectiveness of the approach. The Extra Tree Classifier followed with an accuracy of 89.66%, whereas the Ridge Classifier trailed closely at 88.74%. Additionally, Logistic Regression exhibited a notable accuracy rate of 91.42%, and the SVM model achieved a reasonable accuracy of 91.44%. This approach integrates the feature extraction power of deep learning with the interpretability of the traditional models. The results demonstrate the efficacy of our method in classifying ductal, lobular, and papillary cancers. The proposed method offers a variety of advantages, including early-stage identification, increased precision, customized medical advice, and simplified analysis, by combining feature extraction with ensemble learning. Ongoing research aims to refine these algorithms, leading to earlier detection and improved outcomes. This innovative approach has the potential to revolutionize breast cancer care and fundamentally reshape treatment strategies.