Abstract：
The prevalence of brain tumor disorders is currently a global issue. In general, radiography, which includes a large number of images, is an efficient method for diagnosing these life-threatening disorders. The biggest issue in this area is that it takes a radiologist a long time and is physically strenuous to look at all the images. As a result, research into developing systems based on machine learning to assist radiologists in diagnosis continues to rise daily. Convolutional neural networks (CNNs), one type of deep learning approach, have been pivotal in achieving state-of-the-art results in several medical imaging applications, including the identification of brain tumors. CNN hyperparameters are typically set manually for segmentation and classification, which might take a while and increase the chance of using suboptimal hyperparameters for both tasks. Bayesian optimization is a useful method for updating the deep CNN\'s optimal hyperparameters. The CNN network, however, can be considered a \"black box\" model because of how difficult it is to comprehend the information it stores because of its complexity. Therefore, this problem can be solved by using Explainable Artificial Intelligence (XAI) tools, which provide doctors with a realistic explanation of CNN\'s assessments. Implementation of deep learning-based systems in real-time diagnosis is still rare. One of the causes could be that these methods don\'t quantify the Uncertainty in the predictions, which could undermine trust in the AI-based diagnosis of diseases. To be used in real-time medical diagnosis, CNN-based models must be realistic and appealing, and uncertainty needs to be evaluated. So, a novel three-phase strategy is proposed for segmenting and classifying brain tumors. Segmentation of brain tumors using the DeeplabV3+ model is first performed with tuning of hyperparameters using Bayesian optimization. For classification, features from state-of-the-art deep learning models Darknet53 and mobilenetv2 are extracted and fed to SVM for classification, and hyperparameters of SVM are also optimized using a Bayesian approach. The second step is to understand whatever portion of the images CNN uses for feature extraction using XAI algorithms. Using confusion entropy, the Uncertainty of the Bayesian optimized classifier is finally quantified. Based on a Bayesian-optimized deep learning framework, the experimental findings demonstrate that the proposed method outperforms earlier techniques, achieving a 97 % classification accuracy and a 0.98 global accuracy.
摘要：
目前脑肿瘤疾病的患病率是一个全球性的问题。总的来说,射线照相术,其中包括大量的图像，是诊断这些危及生命的疾病的有效方法。这个领域最大的问题是，放射科医生需要很长时间，并且要查看所有图像都很费力。因此,研究开发基于机器学习的系统，以协助放射科医生诊断每天都在增加。卷积神经网络(CNN)，一种深度学习方法，在几种医学成像应用中取得最先进的成果至关重要，包括脑肿瘤的鉴定.CNN超参数通常是手动设置的，用于分割和分类，这可能需要一段时间，并增加对这两个任务使用次优超参数的机会。贝叶斯优化是一种更新深度CNN最优超参数的有效方法。CNN网络,然而,可以被认为是一个“黑匣子”模型，因为它是多么难以理解它存储的信息，因为它的复杂性。因此,这个问题可以通过使用可解释的人工智能(XAI)工具来解决，这为医生提供了CNN评估的现实解释。在实时诊断中实现基于深度学习的系统仍然很少。原因之一可能是这些方法没有量化预测的不确定性，这可能会破坏人们对基于人工智能的疾病诊断的信任。用于实时医疗诊断，基于CNN的模型必须是现实和有吸引力的，和不确定性需要评估。所以,提出了一种新的三阶段策略来分割和分类脑肿瘤。使用DeeplabV3+模型分割脑肿瘤首先使用贝叶斯优化进行超参数调整。对于分类，从最先进的深度学习模型Darknet53和mobilenetv2中提取特征，并将其提供给SVM进行分类，和支持向量机的超参数也使用贝叶斯方法进行优化。第二步是理解CNN使用XAI算法进行特征提取的图像的任何部分。利用混乱熵，最后对贝叶斯优化分类器的不确定性进行量化。基于贝叶斯优化的深度学习框架，实验结果表明，该方法优于早期技术，实现了97%的分类准确率和0.98的全局准确率。
