Holistic evaluation and generalization enhancement of CART-ANOVA based transfer learning approach for brain tumor classifications

Abstract

This study presents a convolutional neural network (CNN) based on enhanced detection of brain tumors. The promising detection is made possible by the CART-ANOVA technique by applying preprocessing methods to improve testing dataset quality. While utilizing two sources of the dataset for both inter and intra-dataset validation, image sharpening techniques are utilized to refine the Source 2 and Source 1 testing datasets, leading to improved model performance and robustness in brain tumor classification. This paper introduces a hyper-parameter tuning model, designed to determine optimal parameters focusing on batch size and learning rate for the authentic classification. By providing statistical validation, this model ensures the selection of the most effective hyperparameters, leading to superior classification performance. The ResNet18 model was initially trained on one dataset, having 20 % of the data reserved for testing. To further evaluate its robustness and generalizability, the model was tested on a second dataset. The framework produces astonishing results, attaining 99.65 % for four tumor classifications and 98.05 % for seven tumor categories on the dataset from Source 1. The introduced data preprocessing methods resulted in 99.31 % accuracy for four distinct tumor classifications and 98.90 % for seven distinct tumor classifications on Source 2, while also improving Source 1 accuracy to 99.84 % (four-class) and 99.03 % (seven-class). By achieving seven distinct classifications, this work not only improves accuracy and variability but also strengthens model robustness through a rigorous post-validation framework. These advancements offer significant potential for improving brain tumor diagnosis and treatment strategies.