Design of a novel fuzzy ensemble CNN framework for ovarian cancer classification using Tissue Microarray images

Background:

Ovarian cancer remains a significant health concern, with a high mortality rate often attributed to late diagnosis. Tissue Microarray (TMA) images offer a cost-effective diagnostic tool, but their manual analysis is time-consuming and requires expert interpretation. To address this, we aim to develop an automated deep learning solution.

Purpose:

This study seeks to develop a robust deep learning method for classifying ovarian cancer TMA images. Specifically, we compare the performance of different Convolutional Neural Network (CNN) architectures and propose an improved ensemble model to enhance diagnostic accuracy and streamline the clinical workflow.

Methods:

The training dataset comprises 12,710 TMA images sourced from various repositories. These images were meticulously labeled into five distinct categories, CC, EC, HGSC, LGSC, and MC, using original data sources and expert annotations. In the first stage, we trained five CNN models, including our proposed EOC-Net and four transfer learning models: DenseNet121, EfficientNetB0, InceptionV3, and ResNet50-v2. In the second stage, we constructed a fuzzy rank-based ensemble model utilizing the Gamma function to combine the predictions from the individual models, aiming to optimize overall accuracy.

Results:

In the first stage, the models achieved Training Accuracies ranging from 86.95% to 96.29% and Testing Accuracies ranging from 76.25% to 87.05%. Notably, EOC-Net, despite having significantly fewer parameters, emerged as the top-performing model. However, in the second stage, the proposed ensemble model surpassed all individual models, achieving an Accuracy of 88.73%, representing a substantial improvement of 1.68%–12.48%.

Conclusion:

Our study underscores the potential of Deep Learning and Ensemble Learning techniques for accurately classifying ovarian cancer TMA images. The ensemble model’s superior performance demonstrates its ability to enhance diagnostic precision, potentially reducing the workload for clinical experts and improving patient outcomes.