EfficientNetB5-Based Deep Learning for Automated Cancer Detection in Tissue Microarray Images.

BACKGROUND

Tissue microarray (TMA) technology is pivotal in cancer research, enabling simultaneous analysis of multiple samples on a single slide. Manual analysis of TMA images remains time-consuming and subjective. This study aimed to develop a robust deep learning model to automate TMA image analysis, enhance accuracy, and support cancer diagnosis.

METHODS

We utilized the EfficientNetB5 deep learning architecture to classify TMA images from the DigitalSlide01 v1_1M01 dataset, comprising breast cancer, Upper Tract Urothelial Carcinoma (UTUC), and gastric cancer samples. Data augmentation methods, including Mix-Up and Cut-Mix, were employed to improve generalization. Preprocessing involved Otsu thresholding and smart bounding box cropping. The model was trained over 80 epochs using the Adam optimizer, a learning rate scheduler, and categorical cross-entropy loss. Performance was evaluated using 10-fold cross-validation and a separate test set, with accuracy, precision, recall, and F1-score as the primary metrics. Performance was further examined using physical-array-held-out and sample-level analyses.

RESULTS

Across the 10 cross-validation validation folds, mean accuracy and F1-score were 85.93% and 82.78%, respectively. Across the separate test-set evaluations by the ten fold-specific models, mean accuracy and F1-score were 80.08% and 75.13%, respectively. In the sample-level analysis, a 10-model mean-probability ensemble achieved 87.80% accuracy and 78.18% macro F1-score on the 246-core test subset. Cancer precision was 100.00% in the reported ensemble analysis, indicating that no Normal or Others samples were observed among samples predicted as Cancer; this does not imply that all Cancer samples were detected.

CONCLUSIONS

The EfficientNetB5-based framework showed useful aggregate classification performance. These findings support the feasibility of automated TMA image classification and emphasize the need for source-balanced external multicenter validation before broader clinical application.