COVID-19 detection with severity level analysis using the deep features, and wrapper-based selection of ranked features

The SARS-COV-2 virus, which causes COVID-19 disease, continues to threaten the whole world with its mutations. Many methods developed for COVID-19 detection are validated on the data sets generally including severe forms of the disease. Since the severe forms of the disease have prominent signatures on X-ray images, the performance to be achieved is high. To slow the spread of the disease, effective computer-assisted screening tools with the ability to detect the mild and the moderate forms of the disease that do not have prominent signatures are needed. In this work, various pretrained networks, namely GoogLeNet, ResNet18, SqueezeNet, ShuffleNet, EfficientNetB0, and Xception, are used as feature extractors for the COVID-19 detection with severity level analysis. The best feature extraction layer for each pre-trained network is determined to optimize the performance. After that, features obtained by the best layer are selected by following a wrapper-based feature selection strategy using the features ranked based on Laplacian scores. The experimental results achieved on two publicly available data sets including all the forms of COVID-19 disease reveal that the method generalized well on unseen data. Moreover, 66.67%, 90.32%, and 100% sensitivity are obtained in the detection of mild, moderate, and severe cases, respectively

MultiTempLSTM: prediction and compression of multitemporal hyperspectral images using LSTM networks

Since multitemporal hyperspectral imaging has an excellent ability to observe the Earth's surface over time, it has been used for various remote sensing applications. On the other hand, multitemporal hyperspectral images (HSIs) contain HSI sequences acquired multiple times over the same scene, resulting in large amounts of data. Conventional HSI compression methods cannot benefit from temporal correlation, which can be very high, depending on the acquisition cycle. We propose a prediction and compression framework that directly considers temporal correlation for the compression of HSIs. The main objective of the proposed method is to predict each spectral signature in the target HSI from the corresponding spectral signature of the reference HSI using a long short-term memory network model that supports clustering. Then, the residual image between the predicted HSI and the target HSI is quantized and entropy encoded for the compression purpose. The experiments are conducted on a ground-based multitemporal dataset named Noguiero, which contains nine HSIs, in terms of prediction and compression performances. Experiments show that the proposed method not only provides the best quality metrics from the perspective of prediction but also has convincing compression performances compared to the other methods. (C) 2021 Society of Photo-Optical Instrumentation Engineers (SPIE)Scientific and Technological Research Council of Turkey (TUBITAK)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [119E405]This study has been supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Project No. 119E405

Brain tumor classification using the fused features extracted from expanded tumor region

In this study, a brain tumor classification method using the fusion of deep and shallow features is proposed to distinguish between meningioma, glioma, pituitary tumor types and to predict the 1p/19q co-deletion status of LGG tumors. Brain tumors can be located in a different region of the brain, and the texture of the surrounding tissues may also vary. Therefore, the inclusion of surrounding tissues into the tumor region (ROI expansion) can make the features more distinctive. In this work, pre-trained AlexNet, ResNet-18, GoogLeNet, and ShuffleNet networks are used to extract deep features from the tumor regions including its surrounding tissues. Even though the deep features are extremely important in classification, some low-level information regarding tumors may be lost as the network deepens. Accordingly, a shallow network is designed to learn low-level information. Next, in order to compensate the information loss, deep features and shallow features are fused. SVM and k-NN classifiers are trained using the fused feature sets. Experimental results achieved on two publicly available data sets demonstrate that using the feature fusion and the ROI expansion at the same time improves the average sensitivity by about 11.72% (ROI expansion: 8.97%, feature fusion: 2.75%). These results confirm the assumption that the tissues surrounding the tumor region carry distinctive information. Not only that, the missing low-level information can be compensated thanks to the feature fusion. Moreover, competitive results are achieved against state-of-the-art studies when the ResNet-18 is used as the deep feature extractor of our classification framework

Ensemble-LungMaskNet: Automated lung segmentation using ensembled deep encoders

Kocaeli University;Kocaeli University Technopark2021 International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2021 -- 25 August 2021 through 27 August 2021 -- -- 172175Automated lung segmentation has importance because it gives clues about several diseases to the experts. It is the step that comes before further detailed analyses of the lungs. However, segmentation of the lungs is a challenging task since the opacities and consolidations are caused by various lung diseases. As a result, the clarity of the borders of the lungs may be lost which makes the segmentation task difficult. The presence of various medical equipment such as cables in the image is another factor that makes segmentation difficult. Therefore, it is a necessity to develop methods that can handle such situations. Learning the most useful patterns related to various diseases is possible with deep learning methods. Unlike conventional methods, learning the patterns improves the generalization ability of the models on unseen data. For this purpose, a deep segmentation framework including ensembles of pre-trained lightweight networks is proposed for lung region segmentation in this work. The experimental results achieved on two publicly available data sets demonstrate the effectiveness of the proposed framework. © 2021 IEEE

Beyin tümörü bölütleme için hafif ve derin bir model

29th IEEE Conference on Signal Processing and Communications Applications, SIU 2021 -- 9 June 2021 through 11 June 2021 -- -- 170536Brain tumors are one of the major causes of increasing deaths worldwide. It is important to correctly identify cancerous tissues by experts in order to make correct treatment planning and to increase patient survival rates. However, manually tracking and segmentation of cancerous tissues in many sections of volumetric MR data is an error-prone and time-consuming process. Developments in the field of deep learning in recent years allow the tasks performed by humans to be performed with higher accuracy and speeds through the developed automatic systems. In this study, a deep learning-based light-weighted model with 6.78M parameters is proposed for the classification of cancerous tissues in the brain. Cross-validation of the proposed method on a public data set results in 84.61%, 82.54%, and 87.15% Boundary F1, mean IoU, and mean accuracy, respectively, shows the robustness of the proposed model. © 2021 IEEE

3D Object Modeling by Structured Light and Stereo Vision

In this paper, we demonstrate a 3D object modeling system utilizing a setup which consists of two CMOS cameras and a DLP projector by making use of structured light and stereo vision. The calibration of the system is carried out using calibration pattern. The images are taken with stereo camera pair by projecting structured light onto the object and the correspondence problem is solved by both epipolar constraint of stereo vision and gray code constraint of structured light. The first experimental results show that the proposed 3D modeling system is able to model the object successfully

MultiTempGAN: Multitemporal multispectral image compression framework using generative adversarial networks

Multispectral satellites that measure the reflected energy from the different regions on the Earth generate the multispectral (MS) images continuously. The following MS image for the same region can be acquired with respect to the satellite revisit period. The images captured at different times over the same region are called multitemporal images. Traditional compression methods generally benefit from spectral and spatial correlation within the MS image. However, there is also a temporal correlation between multitemporal images. To this end, we propose a novel generative adversarial network (GAN) based prediction method called MultiTempGAN for compression of multitemporal MS images. The proposed method defines a lightweight GAN-based model that learns to transform the reference image to the target image. Here, the generator parameters of MultiTempGAN are saved for the reconstruction purpose in the receiver system. Due to MultiTempGAN has a low number of parameters, it provides efficiency in multitemporal MS image compression. Experiments were carried out on three Sentinel-2 MS image pairs belonging to different geographical regions. We compared the proposed method with JPEG2000-based conventional compression methods and three deep learning methods in terms of signal-tonoise ratio, mean spectral angle, mean spectral correlation, and laplacian mean square error metrics. Additionally, we have also evaluated the change detection performances and visual maps of the methods. Experimental results demonstrate that MultiTempGAN not only achieves the best metric values among the other methods at high compression ratios but also presents convincing performances in change detection applications.Scientific and Technological Research Council of Turkey (TUBITAK)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [119E405]This study has been supported by The Scientific and Technological Research Council of Turkey (TUBITAK) [119E405]

Field programmable gate arrays implementation of two-point non-uniformity correction and bad pixel replacement algorithms

Kocaeli University;Kocaeli University Technopark2021 International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2021 -- 25 August 2021 through 27 August 2021 -- -- 172175In this paper, the hardware architecture for two-point non-uniformity correction (TPNUC) and bad pixel replacement (BPR) algorithms are presented based on field-programmable gate arrays (FPGA) for infrared focal plane arrays (IRFPA). An efficient hardware architecture modeled using C++ in the High-Level Synthesis (HLS) tool is presented. The design is tested on an FPGA fabricated at a 16 nm technology node. The design achieves a maximum frequency of 300 MHz and one pixel per clock. A thermal camera development platform (FullScale USB3A) with a resolution of 640×480 is used as the source for the raw video. The simulation results from MATLAB and FPGA posed close similarities. © 2021 IEEE

Agmatine has beneficial effect on harmaline-induced essential tremor in rat

Essential tremor (ET) is one of the most prevalent movement disorders and the most common cause of abnormal tremors. However, it cannot be treated efficiently with the currently available pharmacotherapy options. The pathophysiology of harmaline-induced tremor, most commonly used model of ET, involves various neurotransmitter systems including glutamate as well as ion channels. Agmatine, an endogenous neuromodulator, interacts with various glutamate receptor subtypes and ion channels, which have been associated with its? beneficial effects on several neurological disorders. The current study aims to assess the effect of agmatine on the harmaline model of ET. Two separate groups of male rats were injected either with saline or agmatine (40 mg/ kg) 30 min prior to single intraperitoneal injection of harmaline (20 mg/kg). The percent duration, intensity and frequency of tremor and locomotor activity were evaluated by a custom-built tremor and locomotion analysis system. Pretreatment with agmatine reduced the percent tremor duration and intensity of tremor induced by harmaline, without affecting the tremor frequency. However, it did not affect the decreased spontaneous locomotor activity due to harmaline. This pattern of ameliorating effects of agmatine on harmaline-induced tremor provide the first evidence for being considered as a treatment option for ET.Scientific Research Projects Coordination Unit of Kocaeli UniversityKocaeli University [KOU BAP 2018/104 HD]This research was supported by grants from the Scientific Research Projects Coordination Unit of Kocaeli University (Project number: KOU BAP 2018/104 HD)