Gencer Kerem, Gencer Gülcan
Afyon Kocatepe University, Faculty of Engineering, Department of Computer Engineering, Afyonkarahisar, Turkey.
Afyonkarahisar Health Sciences University, Faculty of Medicine, Department of Biostatistics and Medical Informatics, Afyonkarahisar, Turkey.
PeerJ Comput Sci. 2025 Jan 21;11:e2556. doi: 10.7717/peerj-cs.2556. eCollection 2025.
One of the most complex and life-threatening pathologies of the central nervous system is brain tumors. Correct diagnosis of these tumors plays an important role in determining the treatment plans of patients. Traditional classification methods often rely on manual assessments, which can be prone to error. Therefore, multiple classification of brain tumors has gained significant interest in recent years in both the medical and computer science fields. The use of artificial intelligence and machine learning, especially in the automatic classification of brain tumors, is increasing significantly. Deep learning models can achieve high accuracy when trained on datasets in diagnosis and classification. This study examined deep learning-based approaches for automatic multi-class classification of brain tumors, and a new approach combining deep learning and quantum genetic algorithms (QGA) was proposed. The powerful feature extraction ability of the pre-trained EfficientNetB0 was utilized and combined with this quantum genetic algorithms, a new approach was proposed. It is aimed to develop the feature selection method. With this hybrid method, high reliability and accuracy in brain tumor classification was achieved. The proposed model achieved high accuracy of 98.36% and 98.25%, respectively, with different data sets and significantly outperformed traditional methods. As a result, the proposed method offers a robust and scalable solution that will help classify brain tumors in early and accurate diagnosis and contribute to the field of medical imaging with patient outcomes.
中枢神经系统最复杂且危及生命的病症之一是脑肿瘤。这些肿瘤的正确诊断在确定患者的治疗方案中起着重要作用。传统的分类方法通常依赖人工评估，这可能容易出错。因此，近年来脑肿瘤的多分类在医学和计算机科学领域都引起了极大的关注。人工智能和机器学习的应用，特别是在脑肿瘤的自动分类方面，正在显著增加。深度学习模型在基于数据集进行诊断和分类训练时可以实现高精度。本研究考察了基于深度学习的脑肿瘤自动多分类方法，并提出了一种将深度学习与量子遗传算法（QGA）相结合的新方法。利用预训练的EfficientNetB0强大的特征提取能力并与这种量子遗传算法相结合，提出了一种新方法。其目的是开发特征选择方法。通过这种混合方法，在脑肿瘤分类中实现了高可靠性和准确性。所提出的模型在不同数据集上分别达到了98.36%和98.25%的高精度，显著优于传统方法。结果，所提出的方法提供了一种强大且可扩展的解决方案，这将有助于在早期和准确诊断中对脑肿瘤进行分类，并为医学成像领域以及患者治疗结果做出贡献。
