Graph convolution network-based surrogate model for natural convection in annuli

Feng, Feng; Li, Yu-Bai; Chen, Zhi-Hua; Wu, Wei-Tao; Peng, Jiang-Zhou; Mei, Mei

doi:10.1016/j.csite.2024.104330

Cited by 1 publication

(1 citation statement)

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“…This means that the model is able to not only predict accurately but is also stable and consistent in its performance. Evaluation of model performance through accuracy and loss graphs is very important to ensure that the resulting model is not only accurate, but also reliable under various conditions [23].…”

Section: Trainingmentioning

confidence: 99%

Developing a classification system for brain tumors using the ResNet152V2 CNN model architecture

Rhomadhon,

Ningtias

2024

J. Soft Comput. Explor.

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According to The American Cancer Society, in 2021 there were 24,530 cases of brain and nervous system tumors. The National Cancer Institute reports that there are approximately 4.4 new cases of brain tumors per 100,000 men and women per year. Brain tumors can be detected using magnetic resonance imaging (MRI), a scanning tool that uses a magnetic field and a computer to record brain images and is able to provide clear visualization of differences in soft tissue such as white matter and gray matter. However, this cannot be done optimally because it still relies on manual analysis, so it cannot classify brain tumor types on larger datasets with the potential for error and a low level of accuracy. To accurately determine the type of brain tumor, a better classification method is needed. The aim of this study is to determine the accuracy of brain tumor calcification using the deep learning model. In this study, the classification of brain tumor types was carried out using the ResNet152V2 convolutional neural network (CNN) model which has a depth of 152 layers. The dataset used in this study was 7,023 MRI images of brain tumors consisting of 1,645 meningiomas, 1,621 gliomas, 1,757 pituitary and 2,000 normal. Research results show an accuracy value of 94.44%, so it can be concluded that the ResNet152V2 model performs well in classifying brain tumor images and can be used as a medium for physicians to more accurately diagnose brain tumor patients more accurately.

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Section: Trainingmentioning

confidence: 99%