Search results for: Micheal Olaolu Arowolo

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</div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 2</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Micheal Olaolu Arowolo</h1> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2</span> Annotations of Gene Pathways Images in Biomedical Publications Using Siamese Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Micheal%20Olaolu%20Arowolo">Micheal Olaolu Arowolo</a>, <a href="https://publications.waset.org/search?q=Muhammad%20Azam"> Muhammad Azam</a>, <a href="https://publications.waset.org/search?q=Fei%20He"> Fei He</a>, <a href="https://publications.waset.org/search?q=Mihail%20Popescu"> Mihail Popescu</a>, <a href="https://publications.waset.org/search?q=Dong%20Xu"> Dong Xu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>As the quantity of biological articles rises, so does the number of biological route figures. Each route figure shows gene names and relationships. Manually annotating pathway diagrams is time-consuming. Advanced image understanding models could speed up curation, but they must be more precise. There is rich information in biological pathway figures. The first step to performing image understanding of these figures is to recognize gene names automatically. Classical optical character recognition methods have been employed for gene name recognition, but they are not optimized for literature mining data. This study devised a method to recognize an image bounding box of gene name as a photo using deep Siamese neural network models to outperform the existing methods using ResNet, DenseNet and Inception architectures, the results obtained about 84% accuracy. </p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Biological%20pathway" title="Biological pathway">Biological pathway</a>, <a href="https://publications.waset.org/search?q=gene%20identification" title=" gene identification"> gene identification</a>, <a href="https://publications.waset.org/search?q=object%20detection" title=" object detection"> object detection</a>, <a href="https://publications.waset.org/search?q=Siamese%20network" title=" Siamese network"> Siamese network</a>, <a href="https://publications.waset.org/search?q=ResNet." title=" ResNet."> ResNet.</a> </p> <a href="https://publications.waset.org/10013264/annotations-of-gene-pathways-images-in-biomedical-publications-using-siamese-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10013264/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10013264/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10013264/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10013264/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10013264/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10013264/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10013264/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10013264/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10013264/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10013264/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10013264.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">270</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1</span> Recognition of Gene Names from Gene Pathway Figures Using Siamese Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Muhammad%20Azam">Muhammad Azam</a>, <a href="https://publications.waset.org/search?q=Micheal%20Olaolu%20Arowolo"> Micheal Olaolu Arowolo</a>, <a href="https://publications.waset.org/search?q=Fei%20He"> Fei He</a>, <a href="https://publications.waset.org/search?q=Mihail%20Popescu"> Mihail Popescu</a>, <a href="https://publications.waset.org/search?q=Dong%20Xu"> Dong Xu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>The number of biological papers is growing quickly, which means that the number of biological pathway figures in those papers is also increasing quickly. Each pathway figure shows extensive biological information, like the names of genes and how the genes are related. However, manually annotating pathway figures takes a lot of time and work. Even though using advanced image understanding models could speed up the process of curation, these models still need to be made more accurate. To improve gene name recognition from pathway figures, we applied a Siamese network to map image segments to a library of pictures containing known genes in a similar way to person recognition from photos in many photo applications. We used a triple loss function and a triplet spatial pyramid pooling network by combining the triplet convolution neural network and the spatial pyramid pooling (TSPP-Net). We compared VGG19 and VGG16 as the Siamese network model. VGG16 achieved better performance with an accuracy of 93%, which is much higher than Optical Character Recognition (OCR) results.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Biological%20pathway" title="Biological pathway">Biological pathway</a>, <a href="https://publications.waset.org/search?q=image%20understanding" title=" image understanding"> image understanding</a>, <a href="https://publications.waset.org/search?q=gene%20name%20recognition" title=" gene name recognition"> gene name recognition</a>, <a href="https://publications.waset.org/search?q=object%20detection" title=" object detection"> object detection</a>, <a href="https://publications.waset.org/search?q=Siamese%20network" title=" Siamese network"> Siamese network</a>, <a href="https://publications.waset.org/search?q=Visual%20Geometry%20Group." title=" Visual Geometry Group."> Visual Geometry Group.</a> </p> <a 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Search results for: Micheal Olaolu Arowolo