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

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.

Challenging the Status Quo: Open Journal Systems for Online Academic Writing Course

“…The selection of Open Journal Systems (OJS) as a platform for developing dissertation proposals considers student characteristics (Man, Nural Azhan and Wan Hamzah, 2019), learning objectives (Manescu, 2013), and resource availability for ensuring high quality (Madusanka et al, 2023). OJS is adaptable to accommodate diverse student characteristics by offering flexibility in terms of time and location, enabling students to access learning materials according to their own schedules and preferences (Owen, 2008).…”