Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Model-free functional MRI analysis based on unsupervised clustering

AbstractConventional model-based or statistical analysis methods for functional MRI (fMRI) are easy to implement, and are effective in analyzing data with simple paradigms. However, they are not applicable in situations in which patterns of neural response are complicated and when fMRI response is unknown. In this paper the “neural gas” network is adapted and rigourosly studied for analyzing fMRI data. The algorithm supports spatial connectivity aiding in the identification of activation sites in functional brain imaging. A comparison of this new method with Kohonen’s self-organizing map and with a fuzzy clustering scheme based on deterministic annealing is done in a systematic fMRI study showing comparative quantitative evaluations. The most important findings in this paper are: (1) both “neural gas” and the fuzzy clustering technique outperform Kohonen’s map in terms of identifying signal components with high correlation to the fMRI stimulus, (2) the “neural gas” outperforms the two other methods with respect to the quantization error, and (3) Kohonen’s map outperforms the two other methods in terms of computational expense. The applicability of the new algorithm is demonstrated on experimental data

Analysis of FMRI Exams Through Unsupervised Learning and Evaluation Index

In the last few years, the clustering of time series has seen significant growth and has proven effective in providing useful information in various domains of use. This growing interest in time series clustering is the result of the effort made by the scientific community in the context of time data mining. For these reasons, the first phase of the thesis focused on the study of the data obtained from fMRI exams carried out in task-based and resting state mode, using and comparing different clustering algorithms: SelfOrganizing map (SOM), the Growing Neural Gas (GNG) and Neural Gas (NG) which are crisp-type algorithms, a fuzzy algorithm, the Fuzzy C algorithm, was also used (FCM). The evaluation of the results obtained by using clustering algorithms was carried out using the Davies Bouldin evaluation index (DBI or DB index). Clustering evaluation is the second topic of this thesis. To evaluate the validity of the clustering, there are specific techniques, but none of these is already consolidated for the study of fMRI exams. Furthermore, the evaluation of evaluation techniques is still an open research field. Eight clustering validation indexes (CVIs) applied to fMRI data clustering will be analysed. The validation indices that have been used are Pakhira Bandyopadhyay Maulik Index (crisp and fuzzy), Fukuyama Sugeno Index, Rezaee Lelieveldt Reider Index, Wang Sun Jiang Index, Xie Beni Index, Davies Bouldin Index, Soft Davies Bouldin Index. Furthermore, an evaluation of the evaluation indices will be carried out, which will take into account the sub-optimal performance obtained by the indices, through the introduction of new metrics. Finally, a new methodology for the evaluation of CVIs will be introduced, which will use an ANFIS model

Taming Wild High Dimensional Text Data with a Fuzzy Lash

The bag of words (BOW) represents a corpus in a matrix whose elements are the frequency of words. However, each row in the matrix is a very high-dimensional sparse vector. Dimension reduction (DR) is a popular method to address sparsity and high-dimensionality issues. Among different strategies to develop DR method, Unsupervised Feature Transformation (UFT) is a popular strategy to map all words on a new basis to represent BOW. The recent increase of text data and its challenges imply that DR area still needs new perspectives. Although a wide range of methods based on the UFT strategy has been developed, the fuzzy approach has not been considered for DR based on this strategy. This research investigates the application of fuzzy clustering as a DR method based on the UFT strategy to collapse BOW matrix to provide a lower-dimensional representation of documents instead of the words in a corpus. The quantitative evaluation shows that fuzzy clustering produces superior performance and features to Principal Components Analysis (PCA) and Singular Value Decomposition (SVD), two popular DR methods based on the UFT strategy

An Intelligent Hybrid Optimization with Deep Learning model-based Schizophrenia Identification from Structural MRI

One of the fatal diseases that claim women while they are pregnant or nursing is schizophrenia. Despite several developments and symptoms, it can be challenging to discern between benign and malignant conditions. The main and most popular imaging method to predict Schizophrenia is MR Images. Furthermore, a few earlier models had a definite accuracy when diagnosing the condition. Stable MRI criteria must also be implemented immediately. Compared to other imaging technologies, the MRI imaging method is the simplest, safest, and most common for predicting Schizophrenia. The following factors are mostly involved in the subprocess for the initial MRI image. Before calculating the length between the sample point and the cluster center, the initial cluster center of the sample is identified. Classification is done according to how far the sample point is from the cluster center. The picture is then generated once the new cluster center has been derived using the classification history and verified to match the cluster convergence condition. A grey wolf optimization-based convolutional neural network approach is offered to get beyond the limitations and find schizophrenia, whether its hazardous or not. Many MRI images or datasets are analyzed in a short time, and the results show a more accurate or higher rate of schizophrenia recognition