Pornima B. Niranjane, Vaishali B. Niranjane, Krushil M. Punwatkar
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引用次数: 0
摘要
人工智能拓展了医学成像的极限和潜力。因此,为了提高诊断脑部异常的性能和准确性,研究人员一直在努力创造一种有效的自动诊断方法。肿瘤的识别和诊断是通过磁共振成像(MRI)来实现的。医学专家可借助数字图像处理技术识别肿瘤并将其分为正常或异常。这项研究的重点是用于脑磁共振成像肿瘤和非肿瘤图像分类的各种神经网络以及混淆矩阵性能评估。在所有分割技术中,大津阈值法被用于分割。在特征提取方面,采用了灰度共现矩阵(GLCM)。本研究采用的分类技术可产生必要的混淆矩阵参数结果,这些参数可用于评估分类器在 F1 分数、准确度、灵敏度和精确度方面的性能。
Study and Performance Evaluation of Brain MRI Images Using Aartificial Intelligence
The limits and potential of medical imaging are expanded by artificial intelligence. Therefore, in an effort to improve the performance and accuracy of diagnosing brain abnormalities, researchers are constantly working to create an effective and automated diagnosis method. Tumour identification and diagnosis have been achieved by the use of magnetic resonance imaging (MRI). Medical professionals can identify and categorise tumours as normal or abnormal with the aid of digital image processing. This research focuses on various neural networks for brain MRI tumour and non-tumour image categorization and confusion matrix performance evaluation. Otsu's thresholding approach is used for segmentation out of all the segmentation techniques. For feature extraction, a grey level co-occurrence matrix (GLCM) is employed. The classification techniques utilized in this study produce the necessary results in terms of confusion matrix parameters, which may be used to assess the classifier's performance in terms of F1 score, accuracy, sensitivity, and precision.