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CT (roentgen-ray computed tomography) A beam of x-rays is shot straight through the brain. As it comes out the other side, the beam is blunted slightly because it has hit dense living tissues on the way through. Blunting or "attenuation" of the x-ray comes from the density of the tissue encountered along the way. Very dense tissue like bone blocks lots of x-rays; grey matter blocks some and fluid even less. X-ray detectors positioned around the circumference of the scanner collect attenuation readings from multiple angles. A computerized algorithm reconstructs an image of each slice

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The advancement of machine and deep learning methods in traffic sign detection is critical for improving road safety and developing intelligent transportation systems. However, the scarcity of a comprehensive and publicly available dataset on Indian traffic has been a significant challenge for researchers in this field. To reduce this gap, we introduced the Indian Road Traffic Sign Detection dataset (IRTSD-Datasetv1), which captures real-world images across diverse conditions.

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This dataset is a valuable resource for those working on daylight illumination-related projects. It includes a comprehensive collection of images captured under various daylight conditions. These images can be used for tasks such as illumination estimation, scene relighting, and object insertion. The dataset features scenes illuminated by different intensities and angles of daylight, providing a rich set of examples for realistic daylight simulation.

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The LuFI-RiverSnap dataset includes close-range river scene images obtained from various devices, such as UAVs, surveillance cameras, smartphones, and handheld cameras, with sizes up to 4624 × 3468 pixels. Several social media images, which are typically volunteered geographic information (VGI), have also been incorporated into the dataset to create more diverse river landscapes from various locations and sources. 

 

Please see the following links: 

 

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This dataset contains MRI scans from 5 MS and 5 NMO cases from the Universiti Teknologi MARA (UiTM) hospital Malaysia. The brain lesions in the MRI scans have been annotated by a consultant radiologist from Pakistan Institute of Medical Sciences (PIMS) Islamabad Pakistan. The ground truth lesion masks are available as png files, whereas the brain scans are available as jpg files.

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 We conducted a retrospective collection, covering 167 children who were examined and treated at the Children's Hospital of Chongqing Medical University from March 12, 2014 to January 7, 2022, with a total of 1634 IRI image sequences. This study has been registered with the Chinese Clinical Trial Registry, registration number ChiCTR2200058971, and complied with the provisions of the Declaration of Helsinki (DoH). The study was approved by the Institutional Ethical Review Board (document number 2022,69), and a waiver of informed consent was obtained.

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The Deepfake-Synthetic-20K dataset significantly contributes to digital forensics and deepfake detection research. It comprises 20,000 high-resolution, synthetic human face images generated using the advanced StyleGAN-2 architecture. This dataset is designed to support the development and evaluation of machine-learning models that can differentiate between real and artificially synthesized human faces. Each image in the dataset has been meticulously crafted to ensure a diverse representation of age, gender, and ethnicity, reflecting the variability seen in global human populations.

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With the fast growth of deep learning, trainable frameworks have been presented to restore hazy images. However, the capability of most existing learning-based methods is limited since the parameters learned in an end-

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The existing datasets lack the diversity required to train the model so that it performs equally well in real fields  under varying environmental conditions. To address this limitation, we propose to collect a small number of in-field data and use the GAN to generate synthetic data for training the deep learning network. To demonstrate the proposed method, a maize dataset 'IIITDMJ_Maize'  was collected using a drone camera under different weather conditions, including both sunny and cloudy days. The recorded video was processed to sample image frames that were later resized to 224 x 224.

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This simulation dataset contains five types of data: resolutions, vessels, vessel stenosis, tumors, and shape combinations. There are a total of 1000 original binary images. Besides, we set different gray values on images with multiple connected domains to simulate different concentration of magnetic nanoparticles. Next, the images are subjected to operations such as image inversion and image rotation. The final dataset contains 20,000 images. we applied the X-space method based on the X-space theory and we generated the simulated image of magnetic particle imaging.

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