A deep learning model to detect the brain tumor based on magnetic resonance images

Kelvin Leonardi Kohsasih, Muhammad Dipo Agung Rizky, Rika Rosnelly, Willy Wira Widjaja
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Abstract

Deep learning techniques have been widely used in everything from analyzing medical information to tools for making medical diagnoses. One of the most feared diseases in modern medicine is a brain tumor. MRI is a radiological method that can be used to identify brain tumors. However, manual segmentation and analysis of MRI images is time-consuming and can only be performed by a professional neuroradiologist. Therefore automatic recognition is required. This study propose a deep learning method based on a hybrid multi-layer perceptron model with Inception-v3 to predict brain tumors using MRI images. The research was conducted by building the Inception-v3 and multilayer perceptron model, and comparing it with the proposed model. The results showed that the hybrid multilayer perceptron model with inception-v3 achieved accuracy, recall, precision, and fi-score of 92%. While the inception-v3 and multilayer perceptron models only obtained 66% and 56% accuracy, respectively. This research shows that the proposed model successfully predicts brain tumors and improves performance
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基于磁共振图像的脑肿瘤深度学习检测模型
深度学习技术已被广泛应用于从分析医学信息到进行医学诊断的工具的各个领域。现代医学中最令人恐惧的疾病之一是脑瘤。MRI是一种可用于识别脑肿瘤的放射学方法。然而,手动分割和分析MRI图像非常耗时,只能由专业的神经放射科医生进行。因此需要自动识别。本研究提出了一种基于Inception-v3的混合多层感知器模型的深度学习方法,用于使用MRI图像预测脑肿瘤。通过建立Inception-v3和多层感知器模型进行研究,并将其与所提出的模型进行比较。结果表明,inception-v3混合多层感知器模型的准确率、召回率、准确率和fi得分均达到92%。而inception-v3和多层感知器模型分别仅获得66%和56%的准确率。这项研究表明,所提出的模型成功地预测了脑肿瘤,并提高了性能
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发文量
47
审稿时长
6 weeks
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