Detection of tumors on brain MRI images using the hybrid convolutional neural network architecture.

By A. Cinar, M. Yıldırım. Medical Hypotheses, 2020

352 citations recorded by the source corpus.

Brain tumor is one of the dangerous and deadly cancer types seen in adults and children. Early and accurate diagnosis of brain tumor is important for the treatment process. It is an important step for specialists to detect the brain tumor using computer aided systems. These systems allow specialists to perform tumor detection more easily. However, mistakes made with traditional methods are also prevented. In this paper, it is aimed to diagnose the brain tumor using MRI images. CNN models, one of the deep learning networks, are used for the diagnosis process. Resnet50 architecture, one of the CNN models, is used as the base. The last 5 layers of the Resnet50 model have been removed and added 8 new layers. With this model, 97.2% accuracy value is obtained. Also, results are obtained with Alexnet, Resnet50, Densenet201, InceptionV3 and Googlenet models. Of all these models, the model developed with the highest performance has classified the brain tumor images. As a result, when analyzed in other studies in the literature, it is concluded that the developed method is effective and can be used in computer-aided systems to detect brain tumor.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Neurology

View Paper: https://www.semanticscholar.org/paper/a1f85d85a9a32bab9a8793421fa2bf7e3502924c

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Neurology

Citations in the corpus

Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging (2023, methodology): “Equations (7)–(11) [34,50] are used to determine each model’s accuracy, precision, sensitivity, specificity, and F1-score in order to evaluate their overall performance:”
Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning Based Approach (2020, background): “…such as MobileNet, DenseNet, Xception, InceptionV3, InceptionResNetV2, ResNet, VGGNet, NASNet are selected for feature extraction with the possibility of transfer learning advantage for limited datasets and also their satisfying performances in diﬀerent computer vision tasks [42, 43, 44, 45].”
MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers (2021, methodology): “Ah-met and Muhammad [30] used different CNN models such as GoogLeNet, Inception V3, DenseNet-201, AlexNet, and ResNet-50 to classify the brain MR images and obtained reasonable accuracies.”