Brain image clustering by wavelet energy and CBSSO optimization algorithm

Previously, the diagnosis of brain abnormality was significantly important in the saving of social and hospital resources. Wavelet energy is known as an effective feature detection which has great efficiency in different utilities. This paper suggests a new method based on wavelet energy to automatically classify magnetic resonance imaging (MRI) brain images into two groups (normal and abnormal), utilizing support vector machine (SVM) classification based on chaotic binary shark smell optimization (CBSSO) to optimize the SVM weights. The results of the suggested CBSSO-based KSVM are compared favorably to several other methods in terms of better sensitivity and authenticity. The proposed CAD system can additionally be utilized to categorize the images with various pathological conditions, types, and illness modes

A brief network analysis of Artificial Intelligence publication

In this paper, we present an illustration to the history of Artificial Intelligence(AI) with a statistical analysis of publish since 1940. We collected and mined through the IEEE publish data base to analysis the geological and chronological variance of the activeness of research in AI. The connections between different institutes are showed. The result shows that the leading community of AI research are mainly in the USA, China, the Europe and Japan. The key institutes, authors and the research hotspots are revealed. It is found that the research institutes in the fields like Data Mining, Computer Vision, Pattern Recognition and some other fields of Machine Learning are quite consistent, implying a strong interaction between the community of each field. It is also showed that the research of Electronic Engineering and Industrial or Commercial applications are very active in California. Japan is also publishing a lot of papers in robotics. Due to the limitation of data source, the result might be overly influenced by the number of published articles, which is to our best improved by applying network keynode analysis on the research community instead of merely count the number of publish.Comment: 18 pages, 7 figure

Model-based Segmentation and Deep Learning for Lung Cancer

Lung cancer is one of the life taking disease and causes more deaths worldwide. Early detection and treatment is necessary to save life. It is very difficult for doctors to interpret and identify diseases using imaging modalities alone. Therefore computer aided diagnosis can assist doctors for the early detection of cancer very accurately. In the proposed work, optimized deformable models and deep learning techniques are applied for the detection and classification of lung cancer. This method involves pre-processing, lung lobe segmentation, lung cancer segmentation, Data augmentation and lung cancer classification. The median filtering is considered for pre-processing and the Bayesian fuzzy clustering is applied for segmenting the lung lobes. The lung cancer segmentation is carried out using Water Cycle Sea Lion Optimization (WSLnO) based deformable model. The data augmentation process is used to augment the size of segmented region in order to perform better classification. The lung cancer classification is done effectively using Shepard Convolutional Neural Network (ShCNN), which is trained by WSLnO algorithm. The proposed WSLnO algorithm is designed by incorporating Water cycle algorithm (WCA) and Sea Lion Optimization (SLnO) algorithm. The performance of the proposed technique is analyzed with various performance metrics and attained the better results in terms of accuracy, sensitivity, specificity and average segmentation accuracy of 0.9303, 0.9123, 0.9133 and 0.9091 respectively

Determining candidate polyp morphology from CT colonography using a level-set method

In this paper we propose a level-set segmentation for polyp candidates in Computer Tomography Colongraphy (CTC). Correct classification of the candidate polyps into polyp and non-polyp is, in most cases, evaluated using shape features. Therefore, accurate recovery of the polyp candidate surface is important for correct classification. The method presented in this paper, evolves a curvature and gradient dependent boundary to recover the surface of the polyp candidate in a level-set framework. The curvature term is computed using a combination of the Mean curvature and the Gaussian curvature. The results of the algorithm were run through a classifier for two complete data-sets and returned 100% sensitivity for polyps greater than 5mm

Imaging time series for the classification of EMI discharge sources

In this work, we aim to classify a wider range of Electromagnetic Interference (EMI) discharge sources collected from new power plant sites across multiple assets. This engenders a more complex and challenging classification task. The study involves an investigation and development of new and improved feature extraction and data dimension reduction algorithms based on image processing techniques. The approach is to exploit the Gramian Angular Field technique to map the measured EMI time signals to an image, from which the significant information is extracted while removing redundancy. The image of each discharge type contains a unique fingerprint. Two feature reduction methods called the Local Binary Pattern (LBP) and the Local Phase Quantisation (LPQ) are then used within the mapped images. This provides feature vectors that can be implemented into a Random Forest (RF) classifier. The performance of a previous and the two new proposed methods, on the new database set, is compared in terms of classification accuracy, precision, recall, and F-measure. Results show that the new methods have a higher performance than the previous one, where LBP features achieve the best outcome