CT Image Classification Based on Stacked Ensemble Of Convolutional Neural Networks

Shomanov, Adai; Kuchenchirekova, Dina; Kurenkov, Andrey

doi:10.1109/smc53654.2022.9945565

Cited by 2 publications

(1 citation statement)

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“…“CT Image Classification Based on Stacked Ensemble Of Convolutional Neural Networks” by Shomanov et al ( 2022 ). First, looked at the model performance of the newest deep learning architectures, such as Inception, VGGNet, MobileNet, Xception, and ResNet50.…”

Section: Resultsmentioning

confidence: 99%

Stacked ensemble deep learning for pancreas cancer classification using extreme gradient boosting

Bakasa,

Viriri

2023

Front. Artif. Intell.

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Ensemble learning aims to improve prediction performance by combining several models or forecasts. However, how much and which ensemble learning techniques are useful in deep learning-based pipelines for pancreas computed tomography (CT) image classification is a challenge. Ensemble approaches are the most advanced solution to many machine learning problems. These techniques entail training multiple models and combining their predictions to improve the predictive performance of a single model. This article introduces the idea of Stacked Ensemble Deep Learning (SEDL), a pipeline for classifying pancreas CT medical images. The weak learners are Inception V3, VGG16, and ResNet34, and we employed a stacking ensemble. By combining the first-level predictions, an input train set for XGBoost, the ensemble model at the second level of prediction, is created. Extreme Gradient Boosting (XGBoost), employed as a strong learner, will make the final classification. Our findings showed that SEDL performed better, with a 98.8% ensemble accuracy, after some adjustments to the hyperparameters. The Cancer Imaging Archive (TCIA) public access dataset consists of 80 pancreas CT scans with a resolution of 512 * 512 pixels, from 53 male and 27 female subjects. A sample of two hundred and twenty-two images was used for training and testing data. We concluded that implementing the SEDL technique is an effective way to strengthen the robustness and increase the performance of the pipeline for classifying pancreas CT medical images. Interestingly, grouping like-minded or talented learners does not make a difference.

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Section: Resultsmentioning

confidence: 99%