Assessing the potential for deep learning and computer vision to identify bumble bee species from images.
Ontology highlight
ABSTRACT:
SUBMITTER: Spiesman BJ
PROVIDER: S-EPMC8027374 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Identifying, counting and measuring particles is an important component of many research studies. Images with particles are usually processed by hand using a software ruler. Automated processing, based on conventional image processing methods (edge detection, segmentation, etc.) are not universal, can only be used on good-quality images and need to set a number of parameters empirically. In this paper, we present results from the application of deep learning to automated recognition of metal nanoparticles deposited on highly oriented pyrolytic graphite on images obtained by scanning tunneling microscopy (STM). We used the Cascade Mask-RCNN neural network. Training was performed on a dataset containing 23 STM images with 5157 nanoparticles. Three images containing 695 nanoparticles were used for verification. As a result, the trained neural network recognized nanoparticles in the verification set with 0.93 precision and 0.78 recall. Predicted contour refining with 2D Gaussian function was a proposed option. The accuracies for mean particle size calculated from predicted contours compared with ground truth were in the range of 0.87-0.99. The results were compared with outcomes from other generally available software, based on conventional image processing methods. The advantages of deep learning methods for automatic particle recognition were clearly demonstrated. We developed a free open-access web service "ParticlesNN" based on the trained neural network, which can be used by any researcher in the world.
| S-EPMC7408120 | biostudies-literature
Project description:Openly available community science digital vouchers provide a wealth of data to study phenotypic change across space and time. However, extracting phenotypic data from these resources requires significant human effort. Here, we demonstrate a workflow and computer vision model for automatically categorizing species color pattern from community science images. Our work is focused on documenting the striped/unstriped color polymorphism in the Eastern Red-backed Salamander (Plethodon cinereus). We used an ensemble convolutional neural network model to analyze this polymorphism in 20,318 iNaturalist images. Our model was highly accurate (∼98%) despite image heterogeneity. We used the resulting annotations to document extensive niche overlap between morphs, but wider niche breadth for striped morphs at the range-wide scale. Our work showcases key design principles for using machine learning with heterogeneous community science image data to address questions at an unprecedented scale.
| S-EPMC9379571 | biostudies-literature
Project description:Computer vision algorithms can quickly analyze numerous images and identify useful information with high accuracy. Recently, computer vision has been used to identify 2D materials in microscope images. 2D materials have important fundamental properties allowing for their use in many potential applications, including many in quantum information science and engineering. One such material is hexagonal boron nitride (hBN), an isomorph of graphene with a very indistinguishable layered structure. In order to use these materials for research and product development, the most effective method is mechanical exfoliation where single-layer 2D crystallites must be prepared through an exfoliation procedure and then identified using reflected light optical microscopy. Performing these searches manually is a time-consuming and tedious task. Deploying deep learning-based computer vision algorithms for 2D material search can automate the flake detection task with minimal need for human intervention. In this work, we have implemented a new deep learning pipeline to classify crystallites of hBN based on coarse thickness classifications in reflected-light optical micrographs. We have used DetectoRS as the object detector and trained it on 177 images containing hexagonal boron nitride (hBN) flakes of varying thickness. The trained model achieved a high detection accuracy for the rare category of thin flakes ([Formula: see text] atomic layers thick). Further analysis shows that our proposed pipeline could be generalized to various microscope settings and is robust against changes in color or substrate background.
| S-EPMC9884302 | biostudies-literature
Project description:Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief overview of some of the most significant deep learning schemes used in computer vision problems, that is, Convolutional Neural Networks, Deep Boltzmann Machines and Deep Belief Networks, and Stacked Denoising Autoencoders. A brief account of their history, structure, advantages, and limitations is given, followed by a description of their applications in various computer vision tasks, such as object detection, face recognition, action and activity recognition, and human pose estimation. Finally, a brief overview is given of future directions in designing deep learning schemes for computer vision problems and the challenges involved therein.
| S-EPMC5816885 | biostudies-literature
Project description:Most animal species on Earth are insects, and recent reports suggest that their abundance is in drastic decline. Although these reports come from a wide range of insect taxa and regions, the evidence to assess the extent of the phenomenon is sparse. Insect populations are challenging to study, and most monitoring methods are labor intensive and inefficient. Advances in computer vision and deep learning provide potential new solutions to this global challenge. Cameras and other sensors can effectively, continuously, and noninvasively perform entomological observations throughout diurnal and seasonal cycles. The physical appearance of specimens can also be captured by automated imaging in the laboratory. When trained on these data, deep learning models can provide estimates of insect abundance, biomass, and diversity. Further, deep learning models can quantify variation in phenotypic traits, behavior, and interactions. Here, we connect recent developments in deep learning and computer vision to the urgent demand for more cost-efficient monitoring of insects and other invertebrates. We present examples of sensor-based monitoring of insects. We show how deep learning tools can be applied to exceptionally large datasets to derive ecological information and discuss the challenges that lie ahead for the implementation of such solutions in entomology. We identify four focal areas, which will facilitate this transformation: 1) validation of image-based taxonomic identification; 2) generation of sufficient training data; 3) development of public, curated reference databases; and 4) solutions to integrate deep learning and molecular tools.
| S-EPMC7812775 | biostudies-literature
Project description:Gut bacterial communities of bumble bees are correlated with defense against pathogens. Further understanding this host-microbe association is vitally important as bumble bees are currently experiencing global population declines, potentially due in part to emergent diseases. In this study, we used pyrosequencing and community fingerprinting (ARISA) to characterize the gut microbial communities of nine bumble species from across the Bombus phylogeny. Overall, we delimited 74 bacterial taxa (operational taxonomic units or OTUs) belonging to Betaproteobacteria, Gammaproteobacteria, Bacilli, Actinobacteria, Flavobacteria and Alphaproteobacteria. Each bacterial community was taxonomically simple, containing an average of 1.9 common (relative abundance per sample > 5%) bacterial OTUs. The most abundant and prevalent (occurring in 92% of the samples) bacterial OTU, based on 16S rRNA sequences, closely matched that of the previously described Betaproteobacteria species Snodgrassella alvi. Bacteria that were first described in bee-related external environments dominated a number of gut bacterial communities, suggesting that they are not strictly dependent on the internal gut environment. The ARISA data showed a correlation between bacterial community structures and the geographic locations where the bees were sampled, suggesting that at least a subset of the bacterial species may be transmitted environmentally. Using light and fluorescent microscopy, we demonstrated that the gut bacteria form a biofilm on the internal epithelial surface of the ileum, corroborating results obtained from Apis mellifera.
| S-EPMC4359114 | biostudies-literature
Project description:BackgroundColposcopy is widely used to detect cervical cancer, but developing countries lack the experienced colposcopists necessary for accurate diagnosis. Artificial intelligence (AI) is being widely used in computer-aided diagnosis (CAD) systems. In this study, we developed and validated a CAD model based on deep learning to classify cervical lesions on colposcopy images.MethodsPatient data, including clinical information, colposcopy images, and pathological results, were collected from Qilu Hospital. The study included 15,276 images from 7,530 patients. We performed two tasks in this study: normal cervix (NC) vs. low grade squamous intraepithelial lesion or worse (LSIL+) and high-grade squamous intraepithelial lesion (HSIL)- vs. HSIL+. The residual neural network (ResNet) probability was calculated for each patient to reflect the probability of lesions through a ResNet model. Next, a combination model was constructed by incorporating the ResNet probability and clinical features. We divided the dataset into a training set, validation set, and testing set at a ratio of 7:1:2. Finally, we randomly selected 300 patients from the testing set and compared the results with the diagnosis of a senior colposcopist and a junior colposcopist.ResultsThe model that combines ResNet and clinical features performs better than ResNet alone. In the classification of NC and LSIL+, the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were 0.953, 0.886, 0.932, 0.846, 0.838, and 0.936, respectively. In the classification of HSIL- and HSIL+, the AUC, accuracy, sensitivity, specificity, PPV, and NPV were 0.900, 0.807, 0.823, 0.800, 0.618, and 0.920, respectively. In the two classification tasks, the diagnostic performance of the model was determined to be comparable to that of the senior colposcopist and exhibited a stronger diagnostic performance than the junior colposcopist.ConclusionsThe CAD system for cervical lesion diagnosis based on deep learning performs well in the classification of cervical lesions and can provide an objective diagnostic basis for colposcopists.
| S-EPMC8339824 | biostudies-literature
Project description:Since its inception, scanning probe microscopy (SPM) has established itself as the tool of choice for probing surfaces and functionalities at the nanoscale. Although recent developments in the instrumentation have greatly improved the metrological aspects of SPM, it is still plagued by the drifts and nonlinearities of the piezoelectric actuators underlying the precise nanoscale motion. In this work, we present an innovative computer-vision-based distortion correction algorithm for offline processing of functional SPM measurements, allowing two images to be directly overlaid with minimal error - thus correlating position with time evolution and local functionality. To demonstrate its versatility, the algorithm is applied to two very different systems. First, we show the tracking of polarisation switching in an epitaxial Pb(Zr0.2Ti0.8)O3 thin film during high-speed continuous scanning under applied tip bias. Thanks to the precise time-location-polarisation correlation we can extract the regions of domain nucleation and track the motion of domain walls until the merging of the latter in avalanche-like events. Secondly, the morphology of surface folds and wrinkles in graphene deposited on a PET substrate is probed as a function of applied strain, allowing the relaxation of individual wrinkles to be tracked.
| S-EPMC5429659 | biostudies-literature
Project description:BackgroundNowadays doctors and radiologists are overwhelmed with a huge amount of work. This led to the effort to design different Computer-Aided Diagnosis systems (CAD system), with the aim of accomplishing a faster and more accurate diagnosis. The current development of deep learning is a big opportunity for the development of new CADs. In this paper, we propose a novel architecture for a convolutional neural network (CNN) ensemble for classifying chest X-ray (CRX) images into four classes: viral Pneumonia, Tuberculosis, COVID-19, and Healthy. Although Computed tomography (CT) is the best way to detect and diagnoses pulmonary issues, CT is more expensive than CRX. Furthermore, CRX is commonly the first step in the diagnosis, so it's very important to be accurate in the early stages of diagnosis and treatment.ResultsWe applied the transfer learning technique and data augmentation to all CNNs for obtaining better performance. We have designed and evaluated two different CNN-ensembles: Stacking and Voting. This system is ready to be applied in a CAD system to automated diagnosis such a second or previous opinion before the doctors or radiology's. Our results show a great improvement, 99% accuracy of the Stacking Ensemble and 98% of accuracy for the the Voting Ensemble.ConclusionsTo minimize missclassifications, we included six different base CNN models in our architecture (VGG16, VGG19, InceptionV3, ResNet101V2, DenseNet121 and CheXnet) and it could be extended to any number as well as we expect extend the number of diseases to detected. The proposed method has been validated using a large dataset created by mixing several public datasets with different image sizes and quality. As we demonstrate in the evaluation carried out, we reach better results and generalization compared with previous works. In addition, we make a first approach to explainable deep learning with the objective of providing professionals more information that may be valuable when evaluating CRXs.
| S-EPMC9568999 | biostudies-literature
Project description:Bees are important plant pollinators in both natural and agricultural ecosystems. Managed and wild bees have experienced high average annual colony losses, population declines, and local extinctions in many geographic regions. Multiple factors, including virus infections, impact bee health and longevity. The majority of bee-infecting viruses are positive-sense single-stranded RNA viruses. Bee-infecting viruses often cause asymptomatic infections but may also cause paralysis, deformity or death. The severity of infection is governed by bee host immune responses and influenced by additional biotic and abiotic factors. Herein, we highlight studies that have contributed to the current understanding of antiviral defense in bees, including the Western honey bee (Apis mellifera), the Eastern honey bee (Apis cerana) and bumble bee species (Bombus spp.). Bee antiviral defense mechanisms include RNA interference (RNAi), endocytosis, melanization, encapsulation, autophagy and conserved immune pathways including Jak/STAT (Janus kinase/signal transducer and activator of transcription), JNK (c-Jun N-terminal kinase), MAPK (mitogen-activated protein kinases) and the NF-?B mediated Toll and Imd (immune deficiency) pathways. Studies in Dipteran insects, including the model organism Drosophila melanogaster and pathogen-transmitting mosquitos, provide the framework for understanding bee antiviral defense. However, there are notable differences such as the more prominent role of a non-sequence specific, dsRNA-triggered, virus limiting response in honey bees and bumble bees. This virus-limiting response in bees is akin to pathways in a range of organisms including other invertebrates (i.e., oysters, shrimp and sand flies), as well as the mammalian interferon response. Current and future research aimed at elucidating bee antiviral defense mechanisms may lead to development of strategies that mitigate bee losses, while expanding our understanding of insect antiviral defense and the potential evolutionary relationship between sociality and immune function.
| S-EPMC6115922 | biostudies-literature