Ensemble Classifier for Real-Time Breast Cancer Classification on Histopathology Images

One of the most prevalent cancers among women worldwide is breast cancer, and treatment and survival rates are significantly boosted by early detection. Addressing this challenge, the emergence of deep learning (DL) models has offered as a powerful solution. This paper presents a deep learning ensemble-based approach for the classification of breast cancer using histopathological images. The pre-trained VGG16 and MobileNetV2 are combined in the ensemble model to perform binary classification. The sequential depth of VGG16 and the depthwise separable convolutions of MobileNetV2 improved generalization and minimized overfitting. The proposed ensemble achieves a maximum accuracy of 99.14%. The results demonstrate the effectiveness of deep learning ensemble in accurately classifying cancerous and non-cancerous breast cancer images. To assist medical professionals in the diagnosis of breast cancer from histopathology images, a web server-based application is designed. The system has a strong frontend built with ReactJS and Bootstrap that makes it easy for users to upload images. It also has a FastAPI backend that works promptly for processing images. The platform allows for the seamless upload of biopsy images, leveraging a backend TensorFlow-based convolutional neural network ensemble to predict whether the biopsy image is cancerous or non-cancerous along with associated confidence scores.