No abstract

Detection of spinal cord injury (SCI) is one of the major problems in MRI images to detect the affected portion of spinal cord regions using feature sets. Automatic detection of spinal cord atrophy is complex due to change in structure, size, and white matter. Delineating gray matter and white matter are the essential factors that influence the detection of spinal cord atrophy and its severity. Automatic segmentation and classification are accurate methods for detecting the severity of the SCI. Hierarchical segmentation, partitioning segmentation, graph, and watershed segmentation methods are used to find the SCI segments in static fixed positions. Also, these segmentation models result in a high false positive rate due to over segmentation features and noise in the segmented regions. Furthermore, these classification methods fail to segment and detect the severity level in the affected region due to over segmentation. In order to overcome these issues, a novel segment-based classification model is required to find the severity of the injury and to predict the disease patterns on the over segmented regions and features. In the present model, a hybrid image threshold technique is used to segment the spinal cord regions for non-linear SVM classification approach. Among the traditional feature segmentation-based classification models, the proposed thresholdbased non-linear SVM has better accuracy for SCI detection. The results proved that the present model is more efficient than the earlier approaches in terms of true positive rate (TP = 0.9783) and accuracy (0.9683).

No abstract

Staggered Segmenting on the programmed spinal rope form is a vital advance for evaluating spinal line decay in different infections. Outlining dark issue (GM) and white issue (WM) is additionally helpful for measuring GM decay or for extricating multiparametric MRI measurements into WMs tracts. Spinal line division in clinical research isn't as created as cerebrum division, anyway with the considerable change of MR groupings adjusted to spinal line MR examinations, the field of spinal rope MR division has progressed extraordinarily inside the most recent decade. Division strategies with variable exactness and level of multifaceted nature have been produced. In this paper, we talked about a portion of the current strategies for line and WM/GM division, including power based, surface-based, and picture based and staggered based techniques. We likewise give suggestions to approving spinal rope division systems, as it is essential to comprehend the inborn qualities of the strategies and to assess their execution and constraints. In conclusion, we represent a few applications in the solid and neurotic spinal string. In this task, an Automatic Spinal Cord Injury (SCI) is identified utilizing a staggered division technique.

In this paper, a hybrid approach of fundus image classification for diabetic retinopathy (DR) lesions is proposed. Laplacian eigenmaps (LE), a nonlinear dimensionality reduction (NDR) technique is applied to a high-dimensional scale invariant feature transform (SIFT) representation of fundus image for lesion classification. The applied NDR technique gives a low-dimensional intrinsic feature vector for lesion classification in fundus images. The publicly available databases are used for demonstrating the implemented strategy. The performance of applied technique can be evaluated based on sensitivity, specificity and accuracy using Support vector classifier. Compared to other feature vectors, the implemented LE-based feature vector yielded better classification performance. The accuracy obtained is 96.6% for SIFT-LE-SVM.

No abstract

An accurate prediction of cardiac disease is a crucial task for medical and research organizations. Cardiac patients are usually facing heart attacks sometimes tends to death. Therefore, a prior stage of heart diagnosis is compulsory, so that model of optimal Deep learning technology is prosperous for the healthcare sector. The earlier models related to this research work are outdated, some applications cannot provide efficient outcomes. The available conventional models like the Genetic algorithm (GA), PSO (particle swarm optimization), RFO (Random Forest optimization), X-boosting. KNN and many available technologies are only dispensing abnormality information but they are not providing location, depth, and affected area dimensions. Moreover, earlier models only supported fixed scanning in radiology not supporting cloud-level deployment. The sensitivity and robustness of diagnosis are very low therefore a DCAlexNet CNN deep learning technology is needed. The deep learning-based classification is performed through the DCAlexNet CNN (convolutional Neural networks) technique. The implementing application is loading training samples from Kaggle or ANDI dataset. The uploaded image samples are pre-processed through resolution, intensity, and brightness adjustment in the python NumPy tool. The. CSV file (text file) is processed through clustering as well as dimensionality adjusting technique. The processed images are segmented through RRF (Restrictive Random Field) technology. The segmentation on images provides features that are loaded in the local server after that saved into CNN memory. Now the .csv file and trained features are applied to DCAlexNet CNN deep learning architecture. The training processing can give information about diseases in the heart and dimensionality of the affected area (depth and location). Now the application is waiting for real-time samples which is nothing but testing, in this testing part locally available affected and healthy heart ultrasound images are given to DCAlexNet CNN. The designed application can easily be identified whether the uploaded image has abnormality or not. The test-based and image-oriented feature fusion can help the application detect heart abnormalities in an easy way. To this feature fusion-based DCAlexNet CNN confusion matrix generates performance measures like accuracy 98.67%, sensitivity 97.45%, Recall 99.34%, and F1 Score 99.34%, these numerical comparison results compete with present technology and outperformance application robustness.

<p>Dissemination weighted MR imaging may build the affectability and explicitness of MR imaging for certain pathologic states of the spinal rope yet is once in a while performed as a result of a few specialized issues. We consequently tried a novel stage explored turn reverberation dispersion weighted interleaved reverberation planar imaging arrangement in seven sound volunteers and six patients with intramedullary injuries. We performed dispersion weighted MR imaging of the spinal string with high spatial goals. Distinctive examples of dissemination irregularities saw in patient investigations bolster the conceivable symptomatic effect of dispersion weighted MR imaging for ailments of the spinal string. MR imaging has turned into the system of decision for imaging the spinal rope on account of a high affectability for pathologic intra medullary changes. In any case, the explicitness of anomalies oftentimes lingers behind when utilizing just regular MR arrangements. Dissemination weighted MR imaging guarantees to supply additional data in light of trademark changes of the clear dispersion coefficient, for example, those showed in intense ischemia, tumors, or sores related among numerous sclerosis. To date, the indicative commitment of dispersion weighted MR imaging has been concerted essentially in the cerebrum since dissemination weighted MR imaging of the spine is in detail every one the more requesting. Both the little size of the spinal rope and movement-initiated antiquities must be considered. We in this manner built up another examination strategy and tried its unwavering quality and potential for adding to the symptomatic workup of patients with spinal rope indications.</p>