Enhanced MRI-based brain tumour classification with a novel Pix2pix generative adversarial network augmentation framework.

IF 4.1 Q1 CLINICAL NEUROLOGY Brain communications Pub Date : 2024-10-24 eCollection Date: 2024-01-01 DOI:10.1093/braincomms/fcae372
Efe Precious Onakpojeruo, Mubarak Taiwo Mustapha, Dilber Uzun Ozsahin, Ilker Ozsahin
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Abstract

The scarcity of medical imaging datasets and privacy concerns pose significant challenges in artificial intelligence-based disease prediction. This poses major concerns to patient confidentiality as there are now tools capable of extracting patient information by merely analysing patient's imaging data. To address this, we propose the use of synthetic data generated by generative adversarial networks as a solution. Our study pioneers the utilisation of a novel Pix2Pix generative adversarial network model, specifically the 'image-to-image translation with conditional adversarial networks,' to generate synthetic datasets for brain tumour classification. We focus on classifying four tumour types: glioma, meningioma, pituitary and healthy. We introduce a novel conditional deep convolutional neural network architecture, developed from convolutional neural network architectures, to process the pre-processed generated synthetic datasets and the original datasets obtained from the Kaggle repository. Our evaluation metrics demonstrate the conditional deep convolutional neural network model's high performance with synthetic images, achieving an accuracy of 86%. Comparative analysis with state-of-the-art models such as Residual Network50, Visual Geometry Group 16, Visual Geometry Group 19 and InceptionV3 highlights the superior performance of our conditional deep convolutional neural network model in brain tumour detection, diagnosis and classification. Our findings underscore the efficacy of our novel Pix2Pix generative adversarial network augmentation technique in creating synthetic datasets for accurate brain tumour classification, offering a promising avenue for improved disease prediction and treatment planning.

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利用新型 Pix2pix 生成式对抗网络增强框架增强基于磁共振成像的脑肿瘤分类。
医学成像数据集的稀缺性和隐私问题给基于人工智能的疾病预测带来了巨大挑战。由于现在有一些工具能够仅通过分析病人的成像数据就能提取病人的信息,这对病人的保密性造成了极大的担忧。为了解决这个问题,我们提出使用生成式对抗网络生成的合成数据作为解决方案。我们的研究开创性地利用新颖的 Pix2Pix 成因对抗网络模型,特别是 "条件对抗网络的图像到图像转换",生成用于脑肿瘤分类的合成数据集。我们将重点放在四种肿瘤类型的分类上:胶质瘤、脑膜瘤、垂体瘤和健康肿瘤。我们引入了一种从卷积神经网络架构发展而来的新型条件深度卷积神经网络架构,用于处理预处理生成的合成数据集和从 Kaggle 存储库中获取的原始数据集。我们的评估指标证明了条件深度卷积神经网络模型在处理合成图像时的高性能,准确率达到了 86%。与最先进的模型(如 Residual Network50、Visual Geometry Group 16、Visual Geometry Group 19 和 InceptionV3)的比较分析表明,我们的条件深度卷积神经网络模型在脑肿瘤检测、诊断和分类方面表现出色。我们的研究结果凸显了我们新颖的 Pix2Pix 生成对抗网络增强技术在创建合成数据集以进行准确脑肿瘤分类方面的功效,为改进疾病预测和治疗规划提供了一条前景广阔的途径。
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CiteScore
7.00
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0.00%
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审稿时长
6 weeks
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Cerebellar activity in hemi-parkinsonian rats during volitional gait and freezing. Cerebellar activity in PINK1 knockout rats during volitional gait. Enhanced MRI-based brain tumour classification with a novel Pix2pix generative adversarial network augmentation framework. The spine-brain axis: is spinal anatomy associated with brain volume? A glycan biomarker predicts cognitive decline in amyloid- and tau-negative patients.
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