Impact of H&E Stain Normalization on Deep Learning Models in Cancer Image Classification: Performance, Complexity, and Trade-Offs

SIMPLE SUMMARY: This research study investigates the impact of stain normalization on deep learning models for cancer image classification by evaluating model performance, complexity, and trade-offs. The primary objective is to assess the improvement in accuracy, performance, and resource optimization of deep learning models through the standardization of visual appearance in histopathology images using stain normalization techniques, alongside batch size and image size optimization. The findings provide valuable insights for selecting appropriate deep learning models in achieving precise cancer classification, considering the effects of H&E stain normalization and computational resource availability. This study contributes to the existing knowledge on the performance, complexity, and trade-offs associated with applying deep learning models to cancer image classification tasks. ABSTRACT: Accurate classification of cancer images plays a crucial role in diagnosis and treatment planning. Deep learning (DL) models have shown promise in achieving high accuracy, but their performance can be influenced by variations in Hematoxylin and Eosin (H&E) staining techniques. In this study, we investigate the impact of H&E stain normalization on the performance of DL models in cancer image classification. We evaluate the performance of VGG19, VGG16, ResNet50, MobileNet, Xception, and InceptionV3 on a dataset of H&E-stained cancer images. Our findings reveal that while VGG16 exhibits strong performance, VGG19 and ResNet50 demonstrate limitations in this context. Notably, stain normalization techniques significantly improve the performance of less complex models such as MobileNet and Xception. These models emerge as competitive alternatives with lower computational complexity and resource requirements and high computational efficiency. The results highlight the importance of optimizing less complex models through stain normalization to achieve accurate and reliable cancer image classification. This research holds tremendous potential for advancing the development of computationally efficient cancer classification systems, ultimately benefiting cancer diagnosis and treatment.