Linear vs Nonlinear Extreme Learning Machine for Spectral-Spatial Classification of Hyperspectral Image

As a new machine learning approach, extreme learning machine (ELM) has received wide attentions due to its good performances. However, when directly applied to the hyperspectral image (HSI) classification, the recognition rate is too low. This is because ELM does not use the spatial information which is very important for HSI classification. In view of this, this paper proposes a new framework for spectral-spatial classification of HSI by combining ELM with loopy belief propagation (LBP). The original ELM is linear, and the nonlinear ELMs (or Kernel ELMs) are the improvement of linear ELM (LELM). However, based on lots of experiments and analysis, we found out that the LELM is a better choice than nonlinear ELM for spectral-spatial classification of HSI. Furthermore, we exploit the marginal probability distribution that uses the whole information in the HSI and learn such distribution using the LBP. The proposed method not only maintain the fast speed of ELM, but also greatly improves the accuracy of classification. The experimental results in the well-known HSI data sets, Indian Pines and Pavia University, demonstrate the good performances of the proposed method.Comment: 13 pages,8 figures,3 tables,articl

The Effect of Cone Opsin Mutations on Retinal Structure and the Integrity of the Photoreceptor Mosaic

Purpose. To evaluate retinal structure and photoreceptor mosaic integrity in subjects with OPN1LW and OPN1MW mutations. Methods. Eleven subjects were recruited, eight of whom have been previously described. Cone and rod density was measured using images of the photoreceptor mosaic obtained from an adaptive optics scanning light ophthalmoscope (AOSLO). Total retinal thickness, inner retinal thickness, and outer nuclear layer plus Henle fiber layer (ONL+HFL) thickness were measured using cross-sectional spectral-domain optical coherence tomography (SD-OCT) images. Molecular genetic analyses were performed to characterize the OPN1LW/OPN1MW gene array. Results. While disruptions in retinal lamination and cone mosaic structure were observed in all subjects, genotype-specific differences were also observed. For example, subjects with “L/M interchange” mutations resulting from intermixing of ancestral OPN1LW and OPN1MW genes had significant residual cone structure in the parafovea (∼25% of normal), despite widespread retinal disruption that included a large foveal lesion and thinning of the parafoveal inner retina. These subjects also reported a later-onset, progressive loss of visual function. In contrast, subjects with the C203R missense mutation presented with congenital blue cone monochromacy, with retinal lamination defects being restricted to the ONL+HFL and the degree of residual cone structure (8% of normal) being consistent with that expected for the S-cone submosaic. Conclusions. The photoreceptor phenotype associated with OPN1LW and OPN1MW mutations is highly variable. These findings have implications for the potential restoration of visual function in subjects with opsin mutations. Our study highlights the importance of high-resolution phenotyping to characterize cellular structure in inherited retinal disease; such information will be critical for selecting patients most likely to respond to therapeutic intervention and for establishing a baseline for evaluating treatment efficacy

An Investigation on Disease Diagnosis and Prediction by Using Modified K-Mean clustering and Combined CNN and ELM Classification Techniques

Data analysis is important for managing a lot of knowledge in the healthcare industry. The older medical study favored prediction over processing and assimilating a massive volume of hospital data. The precise research of health data becomes advantageous for early disease identification and patient treatment as a result of the tremendous knowledge expansion in the biological and healthcare fields. But when there are gaps in the medical data, the accuracy suffers. The use of K-means algorithm is modest and efficient to perform. It is appropriate for processing vast quantities of continuous, high-dimensional numerical data. However, the number of clusters in the given dataset must be predetermined for this technique, and choosing the right K is frequently challenging. The cluster centers chosen in the first phase have an impact on the clustering results as well. To overcome this drawback in k-means to modify the initialization and centroid steps in classification technique with combining (Convolutional neural network) CNN and ELM (extreme learning machine) technique is used. To increase this work, disease risk prediction using repository dataset is proposed. We use different types of machine learning algorithm for predicting disease using structured data. The prediction accuracy of using proposed hybrid model is 99.8% which is more than SVM (support vector machine), KNN (k-nearest neighbors), AB (AdaBoost algorithm) and CKN-CNN (consensus K-nearest neighbor algorithm and convolution neural network)

Distinctive Mechanisms and Patterns of Exudative Versus Tractional Intraretinal Cystoid Spaces as Seen With Multimodal Imaging.

PurposeTo determine clear-cut distinctions between tractional and exudative intraretinal cystoid spaces subtypes.DesignRetrospective, multicenter, observational case series.MethodsA cohort of patients diagnosed with intraretinal cystoid spaces and imaged with optical coherence tomography (OCT), fluorescein angiography (FA), blue fundus autofluorescence (BFAF), en face OCT, and OCT angiography (OCT-A) was included in the study. All images were qualitatively and quantitatively evaluated.ResultsIn this study were included 72 eyes of 69 patients. Exudative intraretinal cystoid spaces (36/72 eyes, 50%) displayed a "petaloid" morphology as seen with en face OCT, FA, and BFAF. Tractional intraretinal cystoid spaces (24/72 eyes, 33.3%), displayed a radial "spoke-wheel" en face OCT pattern. There was no leakage with FA and BFAF did not reveal specific patterns. Eyes with full-thickness macular hole (FTMH, 12/72 eyes, 16.7%) displayed a "sunflower" en face OCT appearance. FTMH showed OCT, OCT-A, and BFAF features of both exudative and tractional cystoid spaces, but without any FA leakage. Inner nuclear layer (INL) thickness was significantly lower in tractional cystoid spaces (P < .001). There were a greater number of INL cystoid spaces in both the exudative and FTMH subgroups (P = .001). The surface area of INL cystoid spaces was significantly lower in the tractional subgroup (P < .001). There was a significant reduction of the microvascular density in eyes with exudative vs tractional (P = .002) and FTMH (P < .001) subgroups.ConclusionsExudative and tractional intraretinal cystoid spaces displayed characteristic multimodal imaging features and they may represent 2 different pathologic conditions with equally different clinical implications

Optimal Deep Convolutional Neural Network based Fusion Model for Soil Nutrient Analysis

The vast majority of people in India, agriculture is their main line of work, and it has a large economic impact.. Soil is important for supplying vital nutrients to crops for better yield. Determining soil nutrients is certainly essential for selecting appropriate crops and monitoring growth. Common methods used by agriculturalists are inadequate to satisfy increasing demands and have to obstruct cultivating soil. For a better crop yield, agriculturalists must have an awareness regarding the soil nutrients for a specific crop. There comes a need for using Deep learning methods in soil analysis that would help farmers in the domain. This study introduces an Optimal Deep Convolutional Neural Network Fusion Model for Soil nutrient Type Classification (ODCNNF-STC) technique. The presented ODCNNF-STC technique examines the input soil images to classify them into different nutrients present in the soil. In this approach, the noise present in the soil images are initially filtered using a bilateral filter (BF) followed by contrast enhancement. The preprocessed soil images are fed to the model formed by the fusion of DenseNet201 and InceptionResNetV2 models extracting the soil images that can successfully differentiate soil nutrients. Finally, classification of soil nutrients were performed by three classifiers namely extreme learning machine (ELM), RMSProp optimizer-based 1DCNN, and RMSProp optimizer-based Stacked Auto Encoder (SAE). The experimental validation of ODCNNF-STC method is examined on real-time dataset of soil images with a maximum accuracy of 99.39% over recent methods