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ACHENY: A standard Chenopodiaceae image dataset for deep learning models

ABSTRACT: This paper contains datasets related to the “Efficient Deep Learning Models for Categorizing Chenopodiaceae in the wild” (Heidary-Sharifabad et al., 2021). There are about 1500 species of Chenopodiaceae that are spread worldwide and often are ecologically important. Biodiversity conservation of these species is critical due to the destructive effects of human activities on them. For this purpose, identification and surveillance of Chenopodiaceae species in their natural habitat are necessary and can be facilitated by deep learning. The feasibility of applying deep learning algorithms to identify Chenopodiaceae species depends on access to the appropriate relevant dataset. Therefore, ACHENY dataset was collected from natural habitats of different bushes of Chenopodiaceae species, in real-world conditions from desert and semi-desert areas of the Yazd province of IRAN. This imbalanced dataset is compiled of 27,030 RGB color images from 30 Chenopodiaceae species, each species 300-1461 images. Imaging is performed from multiple bushes for each species, with different camera-to-target distances, viewpoints, angles, and natural sunlight in November and December. The collected images are not pre-processed, only are resized to 224 × 224 dimensions which can be used on some of the successful deep learning models and then were grouped into their respective class. The images in each class are separated by 10% for testing, 18% for validation, and 72% for training. Test images are often manually selected from plant bushes different from the training set. Then training and validation images are randomly separated from the remaining images in each category. The small-sized images with 64 × 64 dimensions also are included in ACHENY which can be used on some other deep models.

SUBMITTER: Heidary-Sharifabad A

PROVIDER: S-EPMC8529081 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background and purposeCOVID-19 is a new strain of viruses that causes life stoppage worldwide. At this time, the new coronavirus COVID-19 is spreading rapidly across the world and poses a threat to people's health. Experimental medical tests and analysis have shown that the infection of lungs occurs in almost all COVID-19 patients. Although Computed Tomography of the chest is a useful imaging method for diagnosing diseases related to the lung, chest X-ray (CXR) is more widely available, mainly due to its lower price and results. Deep learning (DL), one of the significant popular artificial intelligence techniques, is an effective way to help doctors analyze how a large number of CXR images is crucial to performance.Materials and methodsIn this article, we propose a novel perceptual two-layer image fusion using DL to obtain more informative CXR images for a COVID-19 dataset. To assess the proposed algorithm performance, the dataset used for this work includes 87 CXR images acquired from 25 cases, all of which were confirmed with COVID-19. The dataset preprocessing is needed to facilitate the role of convolutional neural networks (CNN). Thus, hybrid decomposition and fusion of Nonsubsampled Contourlet Transform (NSCT) and CNN_VGG19 as feature extractor was used.ResultsOur experimental results show that imbalanced COVID-19 datasets can be reliably generated by the algorithm established here. Compared to the COVID-19 dataset used, the fuzed images have more features and characteristics. In evaluation performance measures, six metrics are applied, such as QAB/F, QMI, PSNR, SSIM, SF, and STD, to determine the evaluation of various medical image fusion (MIF). In the QMI, PSNR, SSIM, the proposed algorithm NSCT + CNN_VGG19 achieves the greatest and the features characteristics found in the fuzed image is the largest. We can deduce that the proposed fusion algorithm is efficient enough to generate CXR COVID-19 images that are more useful for the examiner to explore patient status.ConclusionsA novel image fusion algorithm using DL for an imbalanced COVID-19 dataset is the crucial contribution of this work. Extensive results of the experiment display that the proposed algorithm NSCT + CNN_VGG19 outperforms competitive image fusion algorithms.

Project description:BackgroundThe high-definition standard (HD-standard) scan mode has been proven to display stents better than the standard (STND) scan mode but with more image noise. Deep learning image reconstruction (DLIR) is capable of reducing image noise. This study examined the impact of HD-standard scan mode with DLIR algorithms on stent and coronary artery image quality in coronary computed tomography angiography (CCTA) via a comparison with conventional STND scan mode and adaptive statistical iterative reconstruction-Veo (ASIR-V) algorithms.MethodsThe data of 121 patients who underwent HD-standard mode scans (group A: N=47, with coronary stent) or STND mode scans (group B: N=74, without coronary stent) were retrospectively collected. All images were reconstructed with ASIR-V at a level of 50% (ASIR-V50%) and a level of 80% (ASIR-V80%) and with DLIR at medium (DLIR-M) and high (DLIR-H) levels. The noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), artifact index (AI), and in-stent diameter were measured as objective evaluation parameters. Subjective assessment involved a 5-point scale for overall image quality, image noise, stent appearance, stent artifacts, vascular sharpness, and diagnostic confidence. Diagnostic confidence was evaluated based on the presence or absence of significant stenosis (≥50% lumen reduction). Both subjective and objective evaluations were conducted by two radiologists independently, with kappa and intraclass correlation statistics being used to test the interobserver agreement.ResultsThere were 76 evaluable stents in group A, and the DLIR-H algorithm significantly outperformed other algorithms, demonstrating the lowest noise (41.6±7.1/41.3±7.2) and AI (32.4±8.9/31.2±10.1), the highest SNR (14.6±3.5/15.0±3.5) and CNR (13.6±3.8/13.9±3.8), and the largest in-stent diameter (2.18±0.61/2.19±0.61) in representing true stent diameter (all P values <0.01), as well as the highest score in each subjective evaluation parameter. In group B, a total of 296 coronary arteries were evaluated, and the DLIR-H algorithm provided the best objective image quality, with statistically superior noise, SNR, and CNR compared with the other algorithms (all P values <0.05). Moreover, the HD-standard mode scan with DLIR provided better image quality and a lower radiation dose than did the STND mode scan with ASIR-V (P<0.01).ConclusionsHD-standard scan mode with DLIR-H improves image quality of both stents and coronary arteries on CCTA under a lower radiation dose.

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ACHENY: A standard Chenopodiaceae image dataset for deep learning models

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