Detection of Multiple Sclerosis Based on MRI Images using a Low-Power Analog Integrated Artificial Neural Networks

Brain tumor classification plays an important role in brain cancer diagnosis and treatment. Pathologists typically have to work through numerous pathology images that can be in the order of hundreds or thousands which takes time and is prone to manual error. Here we investigate automating this task given pathology images as well as 3D MRI volumes without lesion maps. We use data provided by the CPM-RadPath 2019 MICCAI challenge. We first evaluate accuracy on the validation dataset with MRI and pathology images separately. We predict the 3D tumor mask with our custom developed tumor segmentation model that we used for the BraTS 2019 challenge. We show that the predicted tumor segmentations give a higher validation accuracy of 77.1% vs. 69.8% with MRI images when trained by a 3D residual convolutional neural network. For pathology images we train a 2D residual network and obtain a 66.2% validation accuracy. In both cases we find high training accuracies above 95% which suggests overfitting. We propose a dual path residual convolutional neural network model that trains simultaneously from both MRI and pathology images and we use a simple method to prevent overfitting. One path of our network is fully 3D and considers 3D tumor segmentations as input while the other path considers pathology images. To prevent overfitting we stop training after 90% training accuracy at the epoch number where our network loss increases in the following one. With this approach we achieve a validation accuracy of 84.9% showing that indeed combining the two image sources yields a better overall accuracy.

Background: A Liver tumor is a dangerous disease that may leads to death, the chances of survival will increase when it is detected in early time. Materials and Methods: In this study, clustering Fuzzy c-mean and ''Self-Organization Feature Map'', HSOFM, which is an unsupervised artificial neural network, based on the histogram of the image, are presented for segmenting, isolating, and then extracting tumors and other abnormal regions in liver images of MRI and CT imaging. In an additional processing, morphological operations were used to achieve the complete final extraction of the isolated regions without any extra pixels that do not belong to the abnormal regions. These two methods were applied on five MRI and four CT scan images. The extracted regions surface areas were calculated and compared with the groundtruth, manually extracted mass regions, to check the goodness adequacy of the adopted methods. The work was achieved by Mat Lab programing environment. Results: The percent relative differences of the extracted abnormal regions by implementing the adopted methods with the groundtruth were ranged from 1.108 % to 3.861 % for the images of MRI, while for CT scan images, the percent relative differences were in the range from 0.732 % to 3.456%. By implementing FCM clustering, the percent relative difference ranged from 0.724% to 4.370 % for three MRI images, while for CT scan images, the percent relative difference was 4.327 % for one of the adopted CT images. Results of implementing HSOFM indicate the high-quality performance of this method with 95 % accuracy. Conclusion: From the results we can conclude that there is an appropriate number of nodes and clusters that is a more adequate choice than others depending on the properties’ intensity variance in each processed image. The comparison between the two implemented methods figures out the superiority of the HSOFM method over FCM. There are some limitations, such as the small size of the abnormal regions as well as the interference of the abnormal regions with the normal regions of the similar intensity that require applying advanced enhancement methods as an additional preprocessing step.

With the rapid growth of medical big data, medical signal processing measurement techniques are facing severe challenges. Enormous medical images are constantly generated by various health monitoring and sensing devices, such as ultrasound, MRI machines. Hence, based on pulse coupled neural network (PCNN) and the classical visual receptive field (CVRF) with the difference of two Gaussians (DOG), a contrast enhancement of MRI image is suggested to improve the accuracy of clinical diagnosis for smarter mobile healthcare. As one premise, the parameters of DOG are estimated from the fundamentals of CVRF; then the PCNN parameters in image enhancement are estimated eventually with the help of DOG. As a result, the MRI images can be enhanced adaptively. Due to the exponential decay of the dynamic threshold and the pulses coupling among neurons, PCNN effectively enhances the contrast of low grey levels in MRI image. Moreover, because of the inhibitory effects from inhibitory region in CVRF, PCNN also effectively preserves the structures such as edges for enhanced results. Experiments on several MRI images show that the proposed method performs better than other methods by improving contrast and preserving structures well.

BackgroundCross‐modality translation between MRI and PET presents significant challenges due to the distinct mechanisms underlying those modalities. Currently, the leading method for this translation is CycleGAN. Previous studies have proved a strong correlation between BBBMs and brain amyloid status measured by PET. However, the impact of blood‐based biomarkers (BBBMs) on PET image synthesis has not yet been thoroughly evaluated. In this study, we propose Plasma CycleGAN, a generative model based on CycleGAN to synthesize PET images from MRI, while also incorporating BBBMs and other clinical covariates. We show that Plasma CycleGAN outperforms the state‐of‐the‐art CycleGAN for MRI‐to‐PET translation. Our method is the first to integrate BBBMs in a conditional cross modality translation from MRI to PET.MethodOur model was built on CycleGAN architecture, while the backbone was improved by integrating BBBMs as additional conditions in the input domain. To be specific, we introduce a masked perturbation in the MRI image, whose voxel‐level magnitudes are learnable parameters in the neural network. The dataset we used to evaluate our model is downloaded from the ADNI database. The cohort contains 160 subjects with matching BBBM, T1‐weighted MRI images, amyloid PET, and clinical features, including age, sex, weights, etc. The synthesized PET images are compared with the ground truth PET images for performance evaluation using traditional metrics like structural similarity index (SSIM), peak‐signal‐to‐noise ratio (PSNR) and mean square error (MSE). In addition, the Pearson correlation coefficients (PCCs) of the masked SUVR ratios were calculated.ResultWe evaluated the baseline CycleGAN model using MRI input, as well as combined inputs of MRI+BBBM and other clinical features. Our model achieved an SSIM of 0.72±0.16 and PSNR of 22.8±2.3 in PET image synthesis task using MRI+ Aβ42/40, both yielding the best results among all input combinations. MRI+sex achieved the best result using MSE measurement. PCCs of all experimental results ranged from 0.44 to 0.5.ConclusionWe introduce Plasma CycleGAN, the first generative model to integrate BBBMs and clinical features in PET image synthesis from MRI images. In PET image synthesis task, our proposed integrated model outperforms the baseline CycleGAN model.

Brain MRI often reveals long-standing diseases of the nervous system, such as multiple sclerosis, dementia, a stroke, and brain malignancies. In addition to that, the most accurate method of brain MRI, besides the diagnosis of pituitary gland diseases, is the method diagnosing the vessels of the brain and eyes and the organs of the inner ear. On the other hand, many methods of loading medical pictures have been developed with brain MRI data, often to diagnose diseases and monitor health via it. Convolutional neural networks belong to deep learning and are widely used for input from the visual domain. The most common use of CNN is in natural language processing and recommendation systems, image classification, medical imaging, and image and video recognition. This work is divided into several parts. The Msoud dataset, used in this study, consists of 7023 MRI images, which were made by the Fighshare, SARTAJ, and Br35H datasets. The MRI images are of four classes, that is, healthy brains, brains with glioma, brains with meningioma, and pituitary. In this research work, the doing of different pre-processing of the MRI input to make the images ready for the model to be trained is done. The architecture is made up of dense layers such that after each set of convolutional layers, there is a max-pooling. Eventually, batch normalization and dropouts in the training are stabilized to reduce overfitting. The proposed CNN compared with other studies and many transfer learning models found the proposed model to achieve significant accuracy of 99.00%, 98% and 97% for using Adamax, Adam and RMSprop optimizers respectively.

Brain tumour classification using conventional techniques involving human intervention is prone to errors. Errors in feature extraction and/or classification may turn out to be fatal. Hence focus has shifted on automated techniques for the feature extraction and classification of brain tumour images. In the present work, a mechanism for the same has been proposed involving Artificial Neural Networks (ANN). Three categories have been incorporated in the present work, viz. normal brain, brain with benign tumour and brain with malignant tumour. In the present case, MRI images of the brain have been used to train an Artificial Neural Network which subsequently predicts the category of some new MRI data. The key challenge in designing such a system is attaining high accuracy of classification. This can be achieved by accurate feature extraction mechanism and then designing an appropriate Neural Network for training and testing. At the outset, segmentation has been employed to separate the region of the tumour (if any). Later the Discrete Wavelet Transform (DWT) is used as a preprocessing tool. The DWT helps in removing the non linearities of the image signal and smoothen it out. Since images exhibit abrupt changes in pixel values, conventional Fourier methods like the Fourier Transform prove to be unfit for image analysis. Subsequently, features like contrast, correlation, energy, homogeneity, mean, standard deviation, entropy, RMS value, variance, smoothness, kurtosis and skewness have been evaluated. Prior to training the Neural Network using the feature values, Principal Component Analysis (PCA) has been employed to find trends in the feature values. A Probabilistic Neural Network (PNN) has been designed and it has been trained using the feature values. I has been found that brain tumour MRI image are classified into normal, benign and malignant category using PNN classifier with an accuracy of 97% for the used data set which can be attributed to the efficacy of the proposed method. Key Words: Brain Tumor Classification, Machine Learning, Feature Extraction, Neural Networks, Confusion Matrix, Classification Accuracy.

Improved diagnosis of tibiofemoral cartilage defects on MRI images using deep learning

A brain tumor occurs when abnormal cells form within the brain. Many people suffer from brain tumor, and it is a serious and dangerous disease. The detection and classification of brain tumor is an important and difficult task in the medical field. The brain tumor detection technique in the MRI images is very significant in many symptomatic and cure applications. But, as the data in MRI images are of highquality, tumor detection and classification are very hard in this process. Medical imaging plays a major role in properly diagnosing the disease, wherein an essential part in detecting the tumor is image segmentation. The segmentation provides an automatic brain tumor detection technique in order to increase the precision of the yields while also decreasing the diagnosis time. The goal is to detect the tumor from the MRI images and extract the features from the segmented tumor and finally classify it. The image detection and classification process includes image acquisition, image pre-processing, denoising, image segmentation, feature extraction and classification. The input image is pre-processed using Weiner and median filtering and the noise is removed using Edge Adaptive Total Variation Denoising (EATVD) technique. Once the noise is removed from the image, it is used for segmentation process, where Mean Shift Clustering is used. The segmented tumor undergoes features extraction stage, where Gray Level Co- occurrence Matrix (GLCM) features are used. In the last stage, images are classified either as tumorous or non-tumorous. Classification is done using Genetic Algorithm Support Vector Machine (GA-SVM), Deep Learning with Convolutional Neural Network (CNN). Early detection of the tumor region can be achieved without much time lapse in the calculation by using this efficient classifier model. This system presents a prototype for detecting objects based on GA-SVM that classifies images and assesses whether the image is cancerous. While comparing the accuracy and computational time of these classifiers, CNN would provide high accuracy and GASVM with lesser simulation time. The simulation results obtained for brain tumor detection and analysis are done with minimum computational time and with reasonable accuracy. This proposed system is tested using SPL dataset, which consists of 20 cases with 40 image samples of T2 FLAIR weighted MRI image and implemented using MATLAB software.

The purpose of the research is to analyze the efficiency of the U-net neural network architecture in decision support systems for glioma diagnostics and segmentation of brain areas affected by it on MRI images.Methods. To conduct experimental studies, a training dataset was generated and the data was normalized. A software implementation of the U-Net neural network architecture was performed using the Keras framework in the Python programming language. The neural network model was trained.Results. A series of experiments were conducted, during which error and classification matrices were obtained, the efficiency of classification of the trained neural network model for the "Tumor" and "No tumor" classes was assessed using metrics such as Recall, Precision and F1-measure, and the quality of segmentation of glioma-affected areas on the test data set was assessed. The quality of segmentation was assessed using the IoU metric, which reflects the ratio of the areas of the bounding boxes and is used to assess the accuracy of the spatial correspondence of the predicted segmented areas highlighted on the masks. Based on the results of testing the neural network model in solving the problem of segmenting brain areas affected by glioma, the average value of the IoU metric was 0.812, which is an acceptable result.Conclusion. The testing results showed that the neural network model based on the U-net architecture is able to effectively diagnose the presence of glioma with acceptable values of the classification and segmentation quality metrics, which indicates the possibility of using this neural network model in medical decision support systems for glioma diagnostics, as well as its segmentation on MRI images. However, it is advisable to refine this neural network model to reduce the number of false negative classification results, which is critically important in medical diagnostics.

Segmentation of a brain tumor is one of the most difficult tasks in the analysis of medical images. The purpose of brain tumor segmentation is to create an accurate outline of brain tumor areas. Gliomas are the most common type of brain tumors. Diagnosis of patients with this disease is based on the analysis of the results of magnetic resonance imaging and segmentation of the tumor boundaries manually. However, due to the time-consuming nature of the manual segmentation process and errors, there is a need for a fast and reliable automatic segmentation algorithm. In recent years, deep learning methods have shown promising effectiveness in solving various computer vision problems, such as image classification, object detection and semantic segmentation. A number of methods based on deep learning have been applied to segmentation of brain tumors, and promising results have been achieved. The article proposes a hybrid method for solving the problem of segmentation of brain tumors based on its MRI images based on the U-Net architecture, the encoder of which uses a model of a deep convolutional neural network pre-trained on a set of ImageNet images. Among such models were used VGG16, VGG19, MobileNetV2, Inception, ResNet50, EfficientNetb7, InceptionResnetV2, DenseNet201, DenseNet121. Based on the hybrid method, the TL-U-Net model was implemented, and numerical experiments were carried out to train it with different encoder models for segmentation of brain tumors based on its MRI images. Computer experiments on a set of MRI images of the brain showed the effectiveness of the proposed approach, the best encoder model turned out to be the neural network Densenet121, which provided indicators of segmentation accuracy MeanIoU=90.34%, MeanDice=94.33%, accuracy=94.17%. The obtained estimates of segmentation accuracy are comparable or exceed similar estimates obtained by other researchers.

The venture suggests an Adhoc technique of MRI brain image classification and image segmentation tactic. It is a programmed structure for phase classification using learning mechanism and to sense the Brain Tumor through spatial fuzzy clustering methods for bio medical applications. Automated classification and recognition of tumors in diverse MRI images is enthused for the high precision when dealing with human life. Our proposal employs a segmentation technique, Spatial Fuzzy Clustering Algorithm, for segmenting MRI images to diagnose the Brain Tumor in its earlier phase for scrutinizing the anatomical makeup. The Artificial Neural Network (ANN) will be exploited to categorize the pretentious tumor part in the brain. Dual Tree-CWT decomposition scheme is utilized for texture scrutiny of an image. Probabilistic Neural Network (PNN) -Radial Basis Function (RBF) will be engaged to execute an automated Brain Tumor classification. The preprocessing steps were operated in two phases: feature mining by means of classification via PNN-RBF network. The functioning of the classifier was assessed with the training performance and classification accuracies.

Nowadays, the number of patients with brain tumors is steadily increasing, diagnosis and isolation of the tumor play an important role in the process of treatment and surgery. Due to the high error of manual segmentation of the tumor, algorithms that perform this operation with less error are of great importance. Convolutional neural networks have made great progress in the field of medical imaging. The use of imaging techniques and pattern recognition in the diagnosis and automatic determination of brain tumors by MRI imaging reduces errors, human error and speeds up detection. The artificial convolutional neural network (CNN) has been widely used in the diagnosis of intelligent cancers and has significantly reduced the error rate. Therefore, in this paper, we present a new method using a combination of convolutional and multi-scale artificial neural network that has significantly increased the accuracy of tumor diagnosis. This study presents a multidisciplinary convolution neural network (MCNN) approach to classifying tumors that can be used as an important part of automated diagnosis systems for accurate cancer diagnosis. Based on the MCNN structure, which presents the MRI image to several deep convolutional neural networks of varying sizes and resolutions, the stage of extracting classical hand-made features is avoided. This approach proposes better classification rates than the classical methods. This study uses a multi-scale convolution technique to achieve a detection accuracy of 95/4%, which shows the efficiency of the proposed method.

Cell is the smallest unit of tissues, whose abnormal growth causes tumor in Brain. Support Vector Machine (SVM) and Artificial Neural Network (ANN) based tumor and its stages classification in brain MRI images is presented in this research work. This work is started with the enhancement of the brain MRI images which are obtained from oncology department of University of Maryland Medical Center. The integration of Temper based K-means and modified Fuzzy C-means (TKFCM) clustering algorithm used to segment the MRI images based on gray level intensity in small portion of brain image. The values of K in Temper based K-means algorithm more than the conventional K-means again, automatically updated membership of FCM eradicates the contouring problem of detecting the tumor region. Then, from the segmented images the first order statistic and region property based features are extracted. The first kind of features is used to detect and isolate tumor from normal brain MRI images with SVM. There is second kind which is used to classify the tumors into benign and four malignant stages tumor with ANN. The accuracy of classifying normal and tumor brain this proposed method is up to 97.37% with Bit Error Rate (BER) of 0.0294 which is better than other methods.

BackgroundMagnetic resonance imaging (MRI) has a wide range of applications in medical imaging. Recently, studies based on deep learning algorithms have demonstrated powerful processing capabilities for medical imaging data. Previous studies have mostly focused on common diseases that usually have large scales of datasets and centralized the lesions in the brain. In this paper, we used deep learning models to process MRI images to differentiate the rare neuromyelitis optical spectrum disorder (NMOSD) from multiple sclerosis (MS) automatically, which are characterized by scattered and overlapping lesions.MethodsWe proposed a novel model structure to capture 3D MRI images’ essential information and converted them into lower dimensions. To empirically prove the efficiency of our model, firstly, we used a conventional 3-dimensional (3D) model to classify the T2-weighted fluid-attenuated inversion recovery (T2-FLAIR) images and proved that the traditional 3D convolutional neural network (CNN) models lack the learning capacity to distinguish between NMOSD and MS. Then, we compressed the 3D T2-FLAIR images by a two-view compression block to apply two different depths (18 and 34 layers) of 2D models for disease diagnosis and also applied transfer learning by pre-training our model on ImageNet dataset.ResultsWe found that our models possess superior performance when our models were pre-trained on ImageNet dataset, in which the models’ average accuracies of 34 layers model and 18 layers model were 0.75 and 0.725, sensitivities were 0.707 and 0.708, and specificities were 0.759 and 0.719, respectively. Meanwhile, the traditional 3D CNN models lacked the learning capacity to distinguish between NMOSD and MS.ConclusionThe novel CNN model we proposed could automatically differentiate the rare NMOSD from MS, especially, our model showed better performance than traditional3D CNN models. It indicated that our 3D compressed CNN models are applicable in handling diseases with small-scale datasets and possess overlapping and scattered lesions.

P055 - Characteristics of a cohort of MS patients in Abu Dhabi