First, the NHL digital pathology images were preprocessed by image division and segmentation and then input into the transfer models for fine-tuning and feature extraction. Second, PCA was used to map the extracted features. Finally, a neural network was used as a classifier to classify the mapped features. During the fine-tuning of the transfer models, two methods, freezing all feature extraction layers and fine-tuning all layers, were employed to select the optimal model with the best classification result among all the pre-selected transfer models. On this basis, the use of freezing the layers’ location was discussed and analyzed.
The results show that the proposed method achieved average 5-fold cross-validation accuracies of 100%, 99.73%, and 99.20% for chronic lymphocytic leukemia (CLL), follicular lymphoma (FL), and mantle cell lymphoma tumor (MCL), and each category has standard deviations 0.00, 0.53, and 0.65, respectively, in the NHL reference dataset. The overall classification accuracy for 5-fold cross-validation is 98.93%, which is an increase of 1.26% compared to the latest reported methods, having a lower standard deviation (1.00).
The method proposed in this paper achieves a high classification accuracy and strong model generalization for the classification of NHL, which makes it possible to conduct intelligent classification of NHL in clinical practice. Our proposed method has definite clinical value and research significance.
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