Benzorgat Nawal, Xia Kewen, Benzorgat Mustapha Noure Eddine
School of Electronics and Information Engineering, Hebei University of Technology, Tianjin, China.
PeerJ Comput Sci. 2024 Nov 27;10:e2425. doi: 10.7717/peerj-cs.2425. eCollection 2024.
Brain tumors are widely recognized as the primary cause of cancer-related mortality globally, necessitating precise detection to enhance patient survival rates. The early identification of brain tumor is presented with significant challenges in the healthcare domain, necessitating the implementation of precise and efficient diagnostic methodologies. The manual identification and analysis of extensive MRI data are presented as a challenging and laborious task, compounded by the importance of early tumor detection in reducing mortality rates. Prompt initiation of treatment hinges upon identifying the specific tumor type in patients, emphasizing the urgency for a dependable deep learning methodology for precise diagnosis. In this research, a hybrid model is presented which integrates the strengths of both transfer learning and the transformer encoder mechanism. After the performance evaluation of the efficacy of six pre-existing deep learning model, both individually and in combination, it was determined that an ensemble of three pretrained models achieved the highest accuracy. This ensemble, comprising DenseNet201, GoogleNet (InceptionV3), and InceptionResNetV2, is selected as the feature extraction framework for the transformer encoder network. The transformer encoder module integrates a Shifted Window-based Self-Attention mechanism, sequential Self-Attention, with a multilayer perceptron layer (MLP). These experiments were conducted on three publicly available research datasets for evaluation purposes. The Cheng dataset, BT-large-2c, and BT-large-4c dataset, each designed for various classification tasks with differences in sample number, planes, and contrast. The model gives consistent results on all three datasets and reaches an accuracy of 99.34%, 99.16%, and 98.62%, respectively, which are improved compared to other techniques.
脑肿瘤被广泛认为是全球癌症相关死亡的主要原因，因此需要精确检测以提高患者生存率。脑肿瘤的早期识别在医疗领域面临重大挑战，需要实施精确且高效的诊断方法。手动识别和分析大量MRI数据是一项具有挑战性且费力的任务，而早期肿瘤检测对于降低死亡率至关重要，这使得情况更加复杂。及时开始治疗取决于确定患者的特定肿瘤类型，这凸显了采用可靠的深度学习方法进行精确诊断的紧迫性。在本研究中，提出了一种混合模型，该模型整合了迁移学习和变压器编码器机制的优势。在对六个现有深度学习模型的有效性进行单独和组合性能评估后，确定由三个预训练模型组成的集成模型实现了最高准确率。这个集成模型由DenseNet201、谷歌网络（InceptionV3）和InceptionResNetV2组成，被选为变压器编码器网络的特征提取框架。变压器编码器模块集成了基于移位窗口的自注意力机制、顺序自注意力以及多层感知器层（MLP）。为了评估目的，这些实验在三个公开可用的研究数据集上进行。程数据集、BT-large-2c和BT-large-4c数据集，每个数据集都针对不同的分类任务进行设计，在样本数量、平面和对比度方面存在差异。该模型在所有三个数据集上都给出了一致的结果，准确率分别达到99.34%、99.16%和98.62%，与其他技术相比有所提高。
