School of Electronics Engineering, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, India.
Comput Biol Med. 2022 Nov;150:106183. doi: 10.1016/j.compbiomed.2022.106183. Epub 2022 Oct 14.
The brain tumor is one of the deadliest diseases of all cancers. Influenced by the recent developments of convolutional neural networks (CNNs) in medical imaging, we have formed a CNN based model called BMRI-Net for brain tumor classification. As the activation function is one of the important modules of CNN, we have proposed a novel parametric activation function named Parametric Flatten-p Mish (PFpM) to improve the performance. PFpM can tackle the significant disadvantages of the pre-existing activation functions like neuron death and bias shift effect. The parametric approach of PFpM also offers the model some extra flexibility to learn the complex patterns more accurately from the data. To validate our proposed methodology, we have used two brain tumor datasets namely Figshare and Br35H. We have compared the performance of our model with state-of-the-art deep CNN models like DenseNet201, InceptionV3, MobileNetV2, ResNet50 and VGG19. Further, the comparative performance of PFpM has been presented with various activation functions like ReLU, Leaky ReLU, GELU, Swish and Mish. We have performed record-wise and subject-wise (patient-level) experiments for Figshare dataset whereas only record-wise experiments have been performed in case of Br35H dataset due to unavailability of subject-wise information. Further, the model has been validated using hold-out and 5-fold cross-validation techniques. On Figshare dataset, our model has achieved 99.57% overall accuracy with hold-out validation and 98.45% overall accuracy with 5-fold cross validation in case of record-wise data split. On the other hand, the model has achieved 97.91% overall accuracy with hold-out validation and 97.26% overall accuracy with 5-fold cross validation in case of subject-wise data split. Similarly, for Br35H dataset, our model has attained 99% overall accuracy with hold-out validation and 98.33% overall accuracy with 5-fold cross validation using record-wise data split. Hence, our findings can introduce a secondary procedure in the clinical diagnosis of brain tumors.
脑肿瘤是所有癌症中最致命的疾病之一。受卷积神经网络（CNN）在医学成像领域的最新发展的影响，我们已经建立了一个基于 CNN 的模型，称为 BMRI-Net，用于脑肿瘤分类。由于激活函数是 CNN 的重要模块之一，我们提出了一种新的参数化激活函数，称为 Parametric Flatten-p Mish (PFpM)，以提高性能。PFpM 可以解决现有激活函数的一些显著缺点，如神经元死亡和偏差漂移效应。PFpM 的参数化方法还为模型提供了一些额外的灵活性，使其能够更准确地从数据中学习复杂的模式。为了验证我们提出的方法，我们使用了两个脑肿瘤数据集，即 Figshare 和 Br35H。我们将我们的模型与最先进的深度学习 CNN 模型（如 DenseNet201、InceptionV3、MobileNetV2、ResNet50 和 VGG19）进行了比较。此外，还展示了 PFpM 与各种激活函数（如 ReLU、Leaky ReLU、GELU、Swish 和 Mish）的比较性能。我们对 Figshare 数据集进行了记录级和主题级（患者级）实验，而由于 Br35H 数据集缺乏主题级信息，仅对记录级实验进行了实验。此外，该模型还使用了保留和 5 倍交叉验证技术进行了验证。在 Figshare 数据集上，我们的模型在记录级数据分割的情况下，使用保留验证达到了 99.57%的整体准确率，使用 5 倍交叉验证达到了 98.45%的整体准确率。另一方面，在主题级数据分割的情况下，该模型在保留验证时达到了 97.91%的整体准确率，在 5 倍交叉验证时达到了 97.26%的整体准确率。类似地，对于 Br35H 数据集，我们的模型在记录级数据分割的情况下，使用保留验证达到了 99%的整体准确率，使用 5 倍交叉验证达到了 98.33%的整体准确率。因此，我们的研究结果可以为脑肿瘤的临床诊断引入一个辅助程序。
