Kar Subrata, Majumder D Dutta
Department of Mathematics, Dumkal Institute of Engineering and Technology, Murshidabad, West Bengal, 742406, India.
Department of Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, West Bengal, India.
Int J Clin Oncol. 2017 Aug;22(4):667-681. doi: 10.1007/s10147-017-1110-5. Epub 2017 Mar 20.
Investigation of brain cancer can detect the abnormal growth of tissue in the brain using computed tomography (CT) scans and magnetic resonance (MR) images of patients. The proposed method classifies brain cancer on shape-based feature extraction as either benign or malignant. The authors used input variables such as shape distance (SD) and shape similarity measure (SSM) in fuzzy tools, and used fuzzy rules to evaluate the risk status as an output variable. We presented a classifier neural network system (NNS), namely Levenberg-Marquardt (LM), which is a feed-forward back-propagation learning algorithm used to train the NN for the status of brain cancer, if any, and which achieved satisfactory performance with 100% accuracy.
The proposed methodology is divided into three phases. First, we find the region of interest (ROI) in the brain to detect the tumors using CT and MR images. Second, we extract the shape-based features, like SD and SSM, and grade the brain tumors as benign or malignant with the concept of SD function and SSM as shape-based parameters. Third, we classify the brain cancers using neuro-fuzzy tools. In this experiment, we used a 16-sample database with SSM (μ) values and classified the benignancy or malignancy of the brain tumor lesions using the neuro-fuzzy system (NFS).
We have developed a fuzzy expert system (FES) and NFS for early detection of brain cancer from CT and MR images. In this experiment, shape-based features, such as SD and SSM, were extracted from the ROI of brain tumor lesions. These shape-based features were considered as input variables and, using fuzzy rules, we were able to evaluate brain cancer risk values for each case. We used an NNS with LM, a feed-forward back-propagation learning algorithm, as a classifier for the diagnosis of brain cancer and achieved satisfactory performance with 100% accuracy. The proposed network was trained with MR image datasets of 16 cases. The 16 cases were fed to the ANN with 2 input neurons, one hidden layer of 10 neurons and 2 output neurons. Of the 16-sample database, 10 datasets for training, 3 datasets for validation, and 3 datasets for testing were used in the ANN classification system. From the SSM (µ) confusion matrix, the number of output datasets of true positive, false positive, true negative and false negative was 6, 0, 10, and 0, respectively. The sensitivity, specificity and accuracy were each equal to 100%.
The method of diagnosing brain cancer presented in this study is a successful model to assist doctors in the screening and treatment of brain cancer patients. The presented FES successfully identified the presence of brain cancer in CT and MR images using the extracted shape-based features and the use of NFS for the identification of brain cancer in the early stages. From the analysis and diagnosis of the disease, the doctors can decide the stage of cancer and take the necessary steps for more accurate treatment. Here, we have presented an investigation and comparison study of the shape-based feature extraction method with the use of NFS for classifying brain tumors as showing normal or abnormal patterns. The results have proved that the shape-based features with the use of NFS can achieve a satisfactory performance with 100% accuracy. We intend to extend this methodology for the early detection of cancer in other regions such as the prostate region and human cervix.
脑癌调查可通过计算机断层扫描（CT）和患者的磁共振（MR）图像检测大脑中组织的异常生长。所提出的方法基于形状特征提取对脑癌进行分类，判断其为良性或恶性。作者在模糊工具中使用形状距离（SD）和形状相似性度量（SSM）等输入变量，并使用模糊规则将风险状态评估为输出变量。我们提出了一种分类器神经网络系统（NNS），即Levenberg-Marquardt（LM），它是一种前馈反向传播学习算法，用于训练神经网络以判断是否存在脑癌，并且该系统以100%的准确率取得了令人满意的性能。
所提出的方法分为三个阶段。首先，我们使用CT和MR图像在大脑中找到感兴趣区域（ROI）以检测肿瘤。其次，我们提取基于形状的特征，如SD和SSM，并以SD函数和SSM作为基于形状的参数的概念将脑肿瘤分为良性或恶性。第三，我们使用神经模糊工具对脑癌进行分类。在本实验中，我们使用了一个包含SSM（μ）值的16个样本的数据库，并使用神经模糊系统（NFS）对脑肿瘤病变的良性或恶性进行分类。
我们开发了一种模糊专家系统（FES）和NFS，用于从CT和MR图像中早期检测脑癌。在本实验中，从脑肿瘤病变的ROI中提取了基于形状的特征，如SD和SSM。这些基于形状的特征被视为输入变量，并使用模糊规则，我们能够评估每个病例的脑癌风险值。我们使用带有LM的NNS（一种前馈反向传播学习算法）作为脑癌诊断的分类器，并以100%的准确率取得了令人满意的性能。所提出的网络使用16例的MR图像数据集进行训练。将这16例输入到具有2个输入神经元、一个包含10个神经元的隐藏层和2个输出神经元的人工神经网络中。在16个样本的数据库中，人工神经网络分类系统使用10个数据集进行训练、3个数据集进行验证、3个数据集进行测试。从SSM（μ）混淆矩阵来看，真阳性、假阳性、真阴性和假阴性的输出数据集数量分别为6、0、10和0。灵敏度、特异性和准确率均等于100%。
本研究中提出的脑癌诊断方法是协助医生对脑癌患者进行筛查和治疗的成功模型。所提出的FES使用提取的基于形状的特征成功识别了CT和MR图像中的脑癌存在情况，并使用NFS在早期阶段识别脑癌。通过对疾病的分析和诊断，医生可以确定癌症阶段并采取必要步骤进行更准确的治疗。在此，我们展示了一项使用NFS对基于形状的特征提取方法进行的调查和比较研究，该方法用于将脑肿瘤分类为显示正常或异常模式。结果证明，使用NFS的基于形状的特征能够以100%的准确率取得令人满意的性能。我们打算将这种方法扩展到其他区域的癌症早期检测，如前列腺区域和人体子宫颈。
