An Ensemble Transfer Learning Model for Brain Tumors Classification using Convolutional Neural Networks

• Autorzy: Sterniczuk Bartosz Piotr , Charytanowicz Małgorzata

Identyfikatory

DOI

10.12913/22998624/193627

• Języki publikacji: EN

Abstrakty

EN

Convolutional neural networks (CNNs) are a specialized class of deep neural networks. In the present era, these have emerged as highly effective tools for a variety of computer vision tasks. Nonetheless, for classification tasks, the application of a single CNN model is often not sufficient to achieve high precision and robustness. Ensemble learning is a machine learning technique that can improve classification performance through combining multiple models into one. With this method, individual models exchange each other's best performance for each class, resulting in improved overall accuracy. In this work, we studied the performance of CNN models for brain tumor classification. As an outcome, we propose a novel ensemble CNN model for this purpose. We utilized the dataset comes from Nanfang Hospital, which include 3064 MRI images categorized into three types of brain tumor (glioma, meningioma and pituitary). First, we assessed well-known CNN models for their ability to classify brain tumors. Next, we tested several ensemble transfer learning models and created one that utilizes the strengths of the most efficient CNN models. The comparative analysis of model performance demonstrated that the examined ensemble CNN models outperformed all single models. Moreover, regarding evaluation metrics, the proposed model achieved global accuracy of 94% and the highest precision and recall, the F1 score of being 94%. Experimental results revealed that model architecture and ensemble methods have a significant impact on brain tumor classification performance.

Słowa kluczowe

EN

convolutional neural network; artificial intelligence; brain tumor; ensemble methods; F1-score

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2024
• Tom: Vol. 18, no. 8
• Strony: 204--216
• Opis fizyczny: Bibliogr. 55 poz., fig., tab.

Twórcy

autor

Sterniczuk Bartosz Piotr

• Department of Computer Science, Lublin University of Technology, Nadbystrzycka 36B, 20-618 Lublin, Poland

• https://orcid.org/0000-0001-7777-6788

autor

Charytanowicz Małgorzata

• Systems Research Institute, Polish Academy of Sciences, Newelska 6, 01-447 Warsaw, Poland
• Department of Computer Science, Lublin University of Technology, Nadbystrzycka 36B, 20-618 Lublin, Poland

• https://orcid.org/0000-0002-1956-3941

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).