Khan Hassan Ali, Jue Wu, Mushtaq Muhammad, Mushtaq Muhammad Umer
School of Computer Science and Technology, Southwest Unversity of Science and Techonlogy, Mianyang 621010, China.
Insitute for Neuro and Bioinformatics, University of Lübeck, Germany.
Math Biosci Eng. 2020 Sep 15;17(5):6203-6216. doi: 10.3934/mbe.2020328.
Brain tumor is a severe cancer disease caused by uncontrollable and abnormal partitioning of cells. Recent progress in the field of deep learning has helped the health industry in Medical Imaging for Medical Diagnostic of many diseases. For Visual learning and Image Recognition, task CNN is the most prevalent and commonly used machine learning algorithm. Similarly, in our paper, we introduce the convolutional neural network (CNN) approach along with Data Augmentation and Image Processing to categorize brain MRI scan images into cancerous and non-cancerous. Using the transfer learning approach we compared the performance of our scratched CNN model with pre-trained VGG-16, ResNet-50, and Inception-v3 models. As the experiment is tested on a very small dataset but the experimental result shows that our model accuracy result is very effective and have very low complexity rate by achieving 100% accuracy, while VGG-16 achieved 96%, ResNet-50 achieved 89% and Inception-V3 achieved 75% accuracy. Our model requires very less computational power and has much better accuracy results as compared to other pre-trained models.
脑肿瘤是一种由细胞不受控制的异常分裂引起的严重癌症疾病。深度学习领域的最新进展有助于医疗行业进行多种疾病的医学成像诊断。对于视觉学习和图像识别，任务卷积神经网络（CNN）是最流行且最常用的机器学习算法。同样，在我们的论文中，我们引入了卷积神经网络（CNN）方法以及数据增强和图像处理，以将脑部磁共振成像（MRI）扫描图像分类为癌性和非癌性。使用迁移学习方法，我们将我们自行构建的CNN模型的性能与预训练的VGG - 16、ResNet - 50和Inception - v3模型进行了比较。由于实验是在非常小的数据集上进行测试的，但实验结果表明，我们的模型准确率结果非常有效，并且通过达到100%的准确率具有非常低的复杂度，而VGG - 16达到了96%，ResNet - 50达到了89%，Inception - V3达到了75%的准确率。与其他预训练模型相比，我们的模型需要的计算能力非常少，并且具有更好的准确率结果。
