Breast Cancer Histopathology Image Classification Using a Set of Deep Learning Models and VGG16 and VGG19 Architectures
M. Alborzi Manesh, Vahid Razmavar · Transactions on Machine Intelligence · 2024
Breast cancer is one of the most prevalent and serious public health challenges worldwide, being the leading cause of cancer-related deaths among women. Early detection is a critical factor in improving survival rates, as it allows for timely intervention and treatment. The complexity of diagnosing breast cancer from histopathology images has led to the development of advanced techniques using artificial intelligence (AI) and machine learning. This study introduces a novel deep learning ensemble approach to classify breast cancer histopathology images using publicly available datasets. The primary objective of this research is to improve the classification accuracy of breast cancer images by leveraging a combination of two deep learning models. The proposed approach utilizes the VGG16 and VGG19 models, which were both fine-tuned to enhance their performance. The results demonstrate that the ensemble method, which averages the predicted probabilities from both models, leads to a more robust classifier. Specifically, the fine-tuning of the VGG16 and VGG19 models contributes significantly to improving the model’s performance. The ensemble model exhibits competitive results in classifying complex histopathology images of breast cancer, with a recall value of 97.73% for the cancer class in both the full training and fine-tuning approaches. This research highlights the effectiveness of ensemble learning in medical image classification, paving the way for more accurate and reliable tools in the diagnosis of breast cancer.