"Intelligent Ultrasound Imaging for Breast Cancer Diagnosis: A Fusion of Ensemble and Transfer Learning Techniques”

ABSTRACT

Breast cancer is one of the leading causes of death among women worldwide, and early detection plays a critical role in improving survival rates. Ultrasound imaging is a widely used, non-invasive diagnostic method, but it requires expert analysis, which can lead to delays or inaccuracies. This project presents an intelligent system for breast cancer diagnosis using ultrasound images by leveraging deep learning techniques, including transfer learning and ensemble methods. The proposed methodology combines feature extraction from pre-trained models (MobileNet, VGG19, InceptionV3) with machine learning classifiers, such as Support Vector Machine (SVM), Random Forest (RF), and Recurrent Neural Networks (RNN). The ensemble approach integrates predictions from all three classifiers using soft voting to improve the accuracy and reliability of the diagnosis. The system classifies breast lesions into three categories: Benign, Malignant, and Normal. The dataset consists of ultrasound images from multiple categories, and the model is trained and evaluated using cross-validation techniques. The results show a significant improvement in accuracy, with the ensemble model achieving a decent accuracy, surpassing the performance of traditional methods. This system aims to assist healthcare professionals by providing accurate, automated classification, ultimately contributing to faster and more reliable breast cancer detection.

Keywords: Ensemble Transfer Learning, Ultrasound Imaging, Breast Cancer Diagnosis, Machine Learning Models, Data Augmentation, Pre-processing Techniques, UBC Benchmark Dataset, Cross-validation, Feature Extraction, Classification Algorithms