Wyniki wyszukiwania - BazTech

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Injury classification and level detection of the spinal cord based on the optimized recurrent neural network

Jasim Munavar, Brindha Thomas

Bio-Algorithms and Med-Systems

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2021

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Vol. 17, no. 1

25--40

EN

Objectives: Spinal cord damage is one of the traumatic situations in persons that may cause the loss of sensation and proper functioning of the muscles either temporarily or permanently. Hence, steps to assure the recovery through the early functioning and precaution could safe-guard a proper interceptive. To ensure the recovery of spinal cord damage through optimized recurrent neural network. Methods: The research on the spinal cord injury classification and level detection is done using the CT images, which is initially given to the segmentation that is done using the adaptive thresholding methodology. Once the segments are formed, the disc is localized using the sparse fuzzy C-means clustering approach. In the next step, the features are extracted from the localized disc and the features include the connectivity features, statistical features, image-level features, grid-level features, Histogram of Oriented Gradients (HOG), and Linear Gradient Pattern (LGP). Then, the injury detection is done based on the Crow search Rider Optimization algorithm-based Deep Convolutional Neural Network (CS-ROA-based DCNN). Once the result regarding the presence of the injury is obtained, the injury-level classification is done based on the proposed Deep Recurrent Neural Network (Deep RNN), and in case of the absence of injury, the process is terminated. Therefore, the injury detection classifier derives the level of the injury, such as normal, wedge, biconcavity, and crush. Results: The experimentation is carried out using an Osteoporotic vertebral fractures database. The performance of the injury level detection based on the proposed model is evaluated based on accuracy, sensitivity, and specificity. The proposed model achieves the maximal accuracy of 0.895, maximal sensitivity of 0.871, and the maximal specificity of 0.933 with respect to K-Fold. Conclusions: The experimental results show that the proposed model is better than the existing models in terms of accuracy, sensitivity, and specificity.

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Multimodal biometric system for identity verification based on hand geometry and hand palm's veins

Christo Luiz Eduardo de, Zimmer Alessandro

Annals of Computer Science and Information Systems

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2017

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Vol. 13

207--212

EN

This project was developed aiming at the implementation of a multibiometric systemcapable to handle hand palm images acquired using a touchless approach. Thisconsiderable increases the difficult of the image processing task due to the fact thatthe images from the same person may vary significantly depeding on the relative position of the hand regarding the sensor. A modular sofware tool was developed, providing the user a method for each of these steps: initialimage preparation, the feature extraction, processing and fusion, ending withthe classification, thus making the researcher'stask a lot easier and faster. The biometric features used for identification includehand geometry features as well palm vein textures. For the hand geometry data, analgorithm for determining finger tips and hand valleys was proposed and from there was possibleto extract a handful of other features related to the geometry of the hand. The handpalm veins' texture features were extracted from a rectangle generated based on thehand's center of mass. The texture descriptor chosen was the Histogram ofGradients. In possession with all the biometric data, the fusion was done on featurelevel. Support Vector Machine technique was used for the classification. Thedatabase chosen for the development of this project was the CASIA Multi- Spectral Palmprint Image Database V1.0. The images used corresponds to the 940nmspectrum due to allowing the visualization of the hand palm's veins. The achievedresult for the hand geometry was an EER of 4,77\%, for the palm veins an EER of3,11\% and changing the threshold value a FAR of 0,50\% and a FRR of 4,82\% wereachieved. For the fusion of both biometrics systems the final result was an EER of 2,33\% witha FAR of 1,30\% and a FRR of 4,27\%.

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Research on human silhouette detection methods for a non-cooperative biometric system

Kacperski D., Włodarczyk M., Grabowski K.

International Journal of Microelectronics and Computer Science

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2015

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Vol. 6, nr 1

10--13

EN

Together with the development of effective and efficient people identification algorithms, biometric authentication systems become increasingly popular and widespread, leading to a significant growth in the number of institutions interested in implementing and using such systems. Although, several research works focused their efforts on these type of solutions, none of the commonly available systems provide a non-cooperative approach to object identification. For this reason, they are not suitable for use in some specific situations, such as people entering the stadium. Therefore, we decided to go up against these limitations and develop biometric identification system for less constrained scenarios. In this paper, we present an evaluation of different algorithms suitable for human silhouette detection in such environment. We focus on investigating their effectiveness and performance under unconstrained conditions, such as different lighting.