Deep Learning-Based Breast Cancer Detection with EfficientNet-B0

Breast cancer ranks second only to lung cancer in both genders, and the early detection of this disorder is closely correlated with the outcome and survival of a patient. Traditional modes of diagnosis have largely relied on manual inspection based upon clinical techniques. These procedures are slow and therefore erratic from the human perspective. This manuscript examines meta-learning, which heralds the dawn of efficient and trustworthy diagnosis of breast cancer by patchbased histopathological image analysis processes. It shows how EfficientNet-B0 can classify breast images in an imbalanced dataset against other features for benign and malignant tumors. The pathological images are taken at four magnifications (40X, 100X, 200X, and 400 X), adding to a pretty rich amount of spatial information for assisting classification tasks. We compare the performance of our EfficientNet-B0-based model with a previously proposed hybrid deep learning and machine learning ensemble approach in a baseline study. The baseline hybrid model performs deep feature extraction from ResNet50V2 in conjunction with ensemble classifiers like LightGBM to perform the classification. Our model achieves a very good accuracy of 96.8 %, considerably higher than the 95 % accuracy shown in the base paper. Besides accuracy, our model gives positive scores on other metrics, including F1-score, precision, and recall, to ensure a more balanced and reliable detection system. These superior results validate that EfficientNet-B0 can do breast cancer detection effectively, with high classification accuracy and computational cost effectiveness, thereby giving it a good chance to be integrated into real clinical applications.