Computer Aided Classifier of Colorectal Cancer on Histopatological Whole Slide Images Analyzing Deep Learning Architecture Parameters
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Show full item recordAuthor
Martínez-Fernández, Elena; Rojas Valenzuela, Ignacio; Valenzuela Cansino, Olga; Rojas Ruiz, IgnacioEditorial
MDPI
Materia
Deep learning Convolutional neural network WSI Cancer Hyperparameters Histopathology images Discriminative fine tuning
Date
2023-04-05Referencia bibliográfica
Martínez-Fernandez, E.; Rojas-Valenzuela, I.; Valenzuela, O.; Rojas, I. Computer Aided Classifier of Colorectal Cancer on Histopatological Whole Slide Images Analyzing Deep Learning Architecture Parameters. Appl. Sci. 2023, 13, 4594. [https://doi.org/10.3390/app13074594]
Sponsorship
Spanish Government PID2021-128317OB-I00; Government of Andalusia P20-00163Abstract
The diagnosis of different pathologies and stages of cancer using whole histopathology
slide images (WSI) is the gold standard for determining the degree of tissue metastasis. The use of
deep learning systems in the field of medical images, especially histopathology images, is becoming
increasingly important. The training and optimization of deep neural network models involve
fine-tuning parameters and hyperparameters such as learning rate, batch size (BS), and boost to
improve the performance of the model in task-specific applications. Tuning hyperparameters is a
major challenge in designing deep neural network models, having a large impact on the performance.
This paper analyzes how the parameters and hyperparameters of a deep learning architecture affect
the classification of colorectal cancer (CRC) histopathology images using the well-known VGG19
model. This paper also discusses the pre-processing of these images, such as the use of color
normalization and stretching transformations on the data set. Among these hyperparameters, the
most important neural network hyperparameter is the learning rate (LR). In this paper, different
strategies for the optimization of LR are analyzed (both static and dynamic) and a new experiment
based on the variation of LR is proposed (the relevance of dynamic strategies over fixed LR is
highlighted), after each layer of the neural network together with decreasing variations according
to the epochs. The results obtained are very remarkable, obtaining in the simulation an accurate
system that achieves 96.4% accuracy on test images (for nine different tissue classes) using the
triangular-cyclic learning rate.