An Optimised InceptionResNetV2 Model for Breast Cancer Histopathology Image Classification
CD Anand, Angeline Kirubha S. P, Glory Precious J, K Christina Evangeline · International Journal of Bioinformatics Research and Applications · 2024
Breast cancer usually develops in women due to uncontrolled cell division. The clinical gold standard for diagnosing this disease is breast histopathology. Automating breast cancer detection saves time and aids pathologists. Deep learning is vital here. This study investigates utilising convolutional neural networks and transfer learning to identify breast cancer from histopathological image patches of all magnifications. Thus, an optimised deep learning model for breast cancer image classification was created by adding and modifying InceptionResNetV2 layers. Transfer learning was used to train and fine-tune it. The model was then compared to VGG-16, DenseNet-121, and original InceptionResNetV2 networks. The optimised InceptionResNetV2 model outperforms all other models with images of all magnification factors. For 400X magnification image classification, the optimised InceptionResNetV2 model has the maximum accuracy of 98%. Hence, the model predicts benign and malignant cancer image patches more accurately.