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Direct-to-patient mobile teledermoscopy is a feasible and useful adjunct to smartphone imaging for monitoring patient-identified lesions of concern, achieving comparable diagnostic and management accuracy as in-office dermatology.

In the present situation human health is a very important factor. With today's growing technology, health conditions are deteriorating day by day. Malnutrition, lifestyle play a very important role. These are leading to many diseases such as respiratory infections, heart disease, diabetes, Cancer, Alzheimer's, strokes and many more. Among these, few diseases are curable, due to the facilities available. Whereas few may be non-curable if care is not taken in the initial stages. Cancer poses the biggest threat among all life-threatening diseases. An efficient lesion segmentation method is introduced for the detection of Melanoma skin cancer disease at the preliminary stages using Adaptive Contour Model (ACM). High-quality segmentation is achieved based on contour features and sharp edge detection using ACM. The performance of the proposed Adaptive Contour Model (ACM) is tested upon PH2 and ISIC Challenge 2017 Dataset. The Performance matrices for the segmentation process are measured in terms of Jaccard index (JA), Dice coefficient (DI) and accuracy that are found to be 79.23, 87.26 and 94.63 considering ISIC dataset and 89.14, 93.98 and 96.95 which is quite high considering PH2 dataset. A method for detection of melanoma has been the critical need of the day. The proposed method shows that the performance of the Adaptive Contour Model (ACM) is more than 95%, which is better than other methods. [ABSTRACT FROM AUTHOR]

In Human Nature, Hobbes argues for what I call the ‘Great Deception Thesis': “whatsoever accidents or qualities our senses make us think there be in the world, they are not there, but are seemings and apparitions only”. I argue that both the thesis and Hobbes’ arguments for it have been misunderstood. Rather than arguing for indirect realism or a primary/secondary quality distinction, Hobbes claims that no sensory experience resembles its object. I conclude by showing how Hobbes can account for the usefulness of images even in the absence of any resemblance between those images and the objects that cause them. If I am right, Hobbes presents a distinctive and intriguing theory of perception, one that emphasizes the dynamic nature of experience. [ABSTRACT FROM AUTHOR]

Brain tumor (BT) is an awful disease and one of the foremost causes of death in human beings. BT develops mainly in 2 stages and varies by volume, form, and structure, and can be cured with special clinical procedures such as chemotherapy, radiotherapy, and surgical mediation. With revolutionary advancements in radiomics and research in medical imaging in the past few years, computer-aided diagnostic systems (CAD), especially deep learning, have played a key role in the automatic detection and diagnosing of various diseases and significantly provided accurate decision support systems for medical clinicians. Thus, convolution neural network (CNN) is a commonly utilized methodology developed for detecting various diseases from medical images because it is capable of extracting distinct features from an image under investigation. In this study, a deep learning approach is utilized to extricate distinct features from brain images in order to detect BT. Hence, CNN from scratch and transfer learning models (VGG-16, VGG-19, and LeNet-5) are developed and tested on brain images to build an intelligent decision support system for detecting BT. Since deep learning models require large volumes of data, data augmentation is used to populate the existing dataset synthetically in order to utilize the best fit detecting models. Hyperparameter tuning was conducted to set the optimum parameters for training the models. The achieved results show that VGG models outperformed others with an accuracy rate of 99.24%, average precision of 99%, average recall of 99%, average specificity of 99%, and average f 1-score of 99% each. The results of the proposed models compared to the other state-of-the-art models in the literature show better performance of the proposed models in terms of accuracy, sensitivity, specificity, and f 1-score. Moreover, comparative analysis shows that the proposed models are reliable in that they can be used for detecting BT as well as helping medical practitioners to diagnose BT. [ABSTRACT FROM AUTHOR]

It is commonly assumed that images, whether in the world or in the head, do not have a privileged analysis into constituent parts. They are thought to lack the sort of syntactic structure necessary for representing complex contents and entering into sophisticated patterns of inference. I reject this assumption. "Image grammars" are models in computer vision that articulate systematic principles governing the form and content of images. These models are empirically credible and can be construed as literal grammars for images. Images can have rich syntactic structure, though of a markedly different form than sentences in language. [ABSTRACT FROM AUTHOR]

In this article, I explore the use, production, and readings of images in antiracist work as processes in constant tension with racist logics. Racist logics are deceptive and entrapping, and the visible world is a particularly dynamic arena in which to analyse this. Images, specifically photographs, incorporate a nuanced and productive contradiction between their ability to illustrate, explain and evoke, on the one hand, while also ensnaring, on the other, partly through alluring and providing pleasure. This contradiction means that anti-racist projects may include the possibility of re-inscribing racist discourse and practice. Drawing on empirical examples from Mexico, this article interrogates how photographic representations of Blackness have been entangled with the racist logics and racial projects that feed them. Racialised images that emphasise the physical body of Black people raise questions about how mestizaje, Latin America's main racial formation, has coded the visual space of representation in confusing ways. [ABSTRACT FROM AUTHOR]

In approximation theory, classical discrete operators, like generalized sampling, Szász‐Mirak'jan, Baskakov, and Bernstein operators, have been extensively studied for scalar functions. In this paper, we look at the approximation of curves by a class of discrete operators, and we exhibit graphical examples concerning several cases. The topic has useful implications about the computer graphics and the image processing: We discuss applications on the approximation and the reconstruction of curves in images. [ABSTRACT FROM AUTHOR]

Increased urbanization coupled with climate change is increasing the number and intensity of stormwater flooding events. Implementing efforts to successfully manage stormwater flooding depends on understanding how people perceive these events. While images of stormwater flooding abound, how these images influence perceptions about flooding events or management options remains understudied. Our objective is to contribute to the general understanding of how various types of images depicting stormwater runoff and stormwater related flooding influence individual and group interpretations of causes of events, major impacts of those events, and responsibility for managing stormwater related events. To this end, we convened focus groups, gave participants numerous photos of stormwater flooding, asked them to identify which images were most concerning, and to then discuss the specific aspects of the photos that prompted concern. We also tested whether a priming image implicating climate change or development as a cause of stormwater flooding influenced viewer reactions. Finally, we asked participants about preferences for who should manage stormwater. Our results revealed that photo location, the water's appearance, and what people were doing in the photo influenced levels of concern. We also found that priming seems to affect opinions regarding urban stormwater management. Finally, there is some evidence that the absence of people in the photo may affect beliefs about who should manage stormwater. [ABSTRACT FROM AUTHOR]

Copyright of Cuadernos del Centro de Estudios de Diseño y Comunicación is the property of Cuadernos del Centro de Estudios de Diseno y Comunicacion and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The role that visual discriminative ability plays among giant pandas in social communication and individual discrimination has received less attention than olfactory and auditory modalities. Here, we used an eye-tracker technology to investigate pupil fixation patterns for 8 captive male giant pandas Ailuropoda melanoleuca. We paired images (N =  26) of conspecifics against: 1) sympatric predators (gray wolves and tigers), and non-threatening sympatric species (golden pheasant, golden snub-nosed monkey, takin, and red panda), 2) conspecifics with atypical fur coloration (albino and brown), and 3) zookeepers/non-zookeepers wearing either work uniform or plain clothing. For each session, we tracked the panda's pupil movements and measured pupil first fixation point (FFP), fixation latency, total fixation count (TFC), and duration (TFD) of attention to each image. Overall, pandas exhibited similar attention (FFPs and TFCs) to images of predators and non-threatening sympatric species. Images of golden pheasant, snub-nosed monkey, and tiger received less attention (TFD) than images of conspecifics, whereas images of takin and red panda received more attention, suggesting a greater alertness to habitat or food competitors than to potential predators. Pandas' TFCs were greater for images of black-white conspecifics than for albino or brown phenotypes, implying that familiar color elicited more interest. Pandas reacted differently to images of men versus women. For images of women only, pandas gave more attention (TFC) to familiar combinations (uniformed zookeepers and plain-clothed non-zookeepers), consistent with the familiarity hypothesis. That pandas can use visual perception to discriminate intra-specifically and inter-specifically, including details of human appearance, has applications for panda conservation and captive husbandry. [ABSTRACT FROM AUTHOR]

Sustainability reporting has been established as a dominant but problematic communication practice in global sustainability management. In this paper, we challenge the commonplace view of reporting as the simple and textual transmission of information, by interpreting corporate sustainability reports as visual artefacts that encourage particular views of environmental issues. We discuss visualization as a key practice in communication and rhetoric, asking what visual managerialism looks like in corporate reports, focusing on a corpus drawn from the Swedish United Nations Global Compact. We engage in a three-stage rhetorical critique to identify three kinds of visualizations: numerical, diagrammatic, and pictorial, establishing how they emphasize communicative characteristics such as logic, simplicity, and clarity. We consolidate these characteristics with the term 'Powerpoint Imagination,' arguing that they construct environmental problems in terms of a technocratic solutionism, i.e., efficiency, standardization and control. We discuss some issues with the Powerpoint Imagination by contrasting it with other images from sustainability reports, concluding with implications for future research. [ABSTRACT FROM AUTHOR]

English as a Foreign Language (EFL) textbook image resources provide important materials and ways of learning for EFL students. Previous studies on the meaning and function of images in Chinese EFL textbooks tend to take pedagogy and cognitive psychology as main perspectives. Based on visual grammar, this article analyzes the image resources in Chinese junior high school EFL textbooks from a multimodal perspective, attempting to explore how the images realize three meta-meanings. It is found that cartoon, photo, table, and diagram are four main types of images in Yilin EFL textbooks. Quantitative analysis and qualitative analysis show that the images in the textbooks realized the representational meaning, interactive meaning and compositional meaning, but there are still some problems in image design, such as insufficient image types, lack of images promoting knowledge comprehension, and inadequate realization of compositional meaning of images. The research results offer some implications for Chinese EFL textbook editors to design textbooks, and also provide some suggestions for Chinese EFL teachers' teaching practice and students' learning. [ABSTRACT FROM AUTHOR]

Images are today at the centre of multiple social and technological tensions as a consequence of the adoption of digital coding, of the massive diffusion of social networks and of the algorithmic processing to which they are subject, resulting in new opportunities for developing analytical inquiries and meaning-producing social actions. In this introduction, the authors intend to reconstruct the broad context that makes images one of the most important resources of the digital era and to focus on some of the research tracks that characterize it. In the first part, they begin by focusing on the relationship between images and the digital, which they retrace in accordance with the selection of three key moments: the transition from ontology to the epistemology of digital media; the opening, by social networks and portable devices, of a field for the computational study of contemporary cultures; and, finally, the analytical potential arising from the encounter between digital archives and computer algorithms. In the second part, they present the three axes around which this issue is structured: archives, identity and algorithms. They first of all discuss the concept of the archive, by presenting four different understandings it has come to bear in conjunction with digital encoding – the archive as heritage, resource, effect and as database. They go on to address the relation between images and identities, arguing that social platforms and visual apps are a new domain for identity experimentation and social aggregation. Finally, they discuss the issue of algorithms and more generally of the new computational economy that associates large amounts of data with their mobilization as operational images. [ABSTRACT FROM AUTHOR]

A novel approach that combines segmented fundus images (FIs) and optical coherence tomography image (OCTIs) are presented here, by incorporating deep learning network (DLN) techniques, to address the imperative need for advanced diagnostic algorithms in detecting and classifying glaucoma. By combining these two images, glaucoma diagnoses are made to improve the accuracy with more reliability. Multi modal convolutional neural networks (MMCNNs) are proposed for automatically extracting discriminatory features from both segmented FIs and OCTIs, allowing for comprehensive ocular analysis. A significant improvement in glaucoma diagnosis is achieved through segmentation of both FIs and OCTIs, ensuring robustness generalization to diverse clinical scenarios, DLN models are trained on datasets encompassing a wide range of glaucoma cases. The integrated approach outperforms individual modalities in terms of early detection of glaucoma and accurate classification. This method demonstrates promising potential in early glaucoma detection due to its effectiveness. By combining segmented features from both FIs and OCTIs through MMCNNs, improved efficiency in diagnosing predominant ocular glaucoma disorder is achieved compared to existing methods. Within the scope of this research, GoogLeNet (GN) is applied to independently classify glaucoma (uni-modal) in segmented FIs and OCTIs, providing a basis for comparison with the evaluation of MMCNNs. [ABSTRACT FROM AUTHOR]

AbstractDespite increasing attention to images in the study of world politics, the role of visual representations in transnational governance processes in general and migration governance, in particular, has received less attention. This paper aims to fill this gap by examining the visual representation of migration governance by the two Canadian government institutions responsible for it: Immigration, Refugees and Citizenship Canada (IRCC) and the Canada Border Services Agency (CBSA). Through a multi-modal analysis of their X (formerly Twitter) images, the paper shows how they tend to shy away from a visual representation of people on the move, privileging a technical communication in an aspiration toward a “neutral” representation of migration issues. Secondly, it sheds light on the discrepancies between the policies directed toward Afghani and Ukrainian refugees and the unexpectedly undifferentiated visual communication about the two groups. Finally, the paper explores what the pictures accompanying X (formerly Twitter) posts of IRCC and CBSA can tell about how these government institutions represent themselves. Findings showed that government institutions’ esthetic practices are not always in line with the institutional migration policies and text narratives, suggesting that different government implementation tools (i.e., regulations, practices, textual discourses) may not reinforce each other but advance alternative perspectives. [ABSTRACT FROM AUTHOR]

Research on political conflict often overlooks the role of visual-based platforms like Instagram in expressing political discontent, focusing primarily on textual content from newspapers and social media. This paper examines the practicalities and challenges of conducting visual research on Instagram, particularly in the context of comparative studies. We highlight the difficulties associated with sampling representative visual content. Through a small case study, we illustrate how hashtags associated with a single country can generate multiple conflicts, using indicators developed in political protest research and contentious politics. The existence of diverse debates within and across hashtags complicates cross-country comparisons of Instagram content and conflict dynamics. To address this issue, we propose an analytical tool for cross-hashtag analysis, allowing for the assessment of degrees of conflict. [ABSTRACT FROM AUTHOR]

This article uses critical discourse analysis to investigate artificial intelligence (AI) generated images of aged care nurses and considers how perspectives and perceptions impact upon the recruitment and retention of nurses. The article demonstrates a recontextualization of aged care nursing, giving rise to hidden ideologies including harmful stereotypes which allow for discrimination and exploitation. It is argued that this may imply that nurses require fewer clinical skills in aged care, diminishing the value of working in this area. AI relies on existing data sets, and thus represent existing stereotypes and biases. The discourse analysis has highlighted key issues which may further impact upon nursing recruitment and retention, and advocates for stronger ethical consideration, including the use of experts in data validation, for the way that aged care services and nurses are depicted and thus valued. [ABSTRACT FROM AUTHOR]

Ischemic brain strokes are severe medical conditions that occur due to blockages in the brain's blood flow, often caused by blood clots or artery blockages. Early detection is crucial for effective treatment. This study aims to improve the detection and classification of ischemic brain strokes in clinical settings by introducing a new approach that integrates the stroke precision enhancement, ensemble deep learning, and intelligent lesion detection and segmentation models. The proposed hybrid model was trained and tested using a dataset of 10,000 computed tomography scans. A 25-fold cross-validation technique was employed, while the model's performance was evaluated using accuracy, precision, recall, and F1 score. The findings indicate significant improvements in accuracy for different stages of stroke images when enhanced using the SPEM model with contrast-limited adaptive histogram equalization set to 4. Specifically, accuracy showed significant improvement (from 0.876 to 0.933) for hyper-acute stroke images; from 0.881 to 0.948 for acute stroke images, from 0.927 to 0.974 for sub-acute stroke images, and from 0.928 to 0.982 for chronic stroke images. Thus, the study shows significant promise for the detection and classification of ischemic brain strokes. Further research is needed to validate its performance on larger datasets and enhance its integration into clinical settings. [ABSTRACT FROM AUTHOR]

With the proliferation of the use of the Internet and social media, the digital media industry has grown enormously in recent years. However, this has brought some challenges, including issues of content security and copyright violation. In this paper, we propose a fusion-based secure watermarking algorithm that we have named "FuSIW". This uses a hashing scheme, to guarantee copyright protection and authentication of RGB images. The algorithm uses the non-subsampled contourlet transform (NSCT) to create a fused watermark image. This contourlet transform (CT) and randomised-singular-value-decomposition (RSVD) based approach allows concealment of the encrypted fused watermark image in the blue channel of the cover image. Subsequently, the hash value of the cover image is inserted into the green channels of the host image. Experimental evaluation indicates that the FuSIW algorithm provides security from geometric attacks and several other common forms of attack. Simulations indicate that the proposed system exhibits improved robustness and security compared to existing methods. [ABSTRACT FROM AUTHOR]

In this piece I reflect on Christoforos Bouzanis' book, Social Imaginary and the Metaphysical Discourse , discussing its main contribution to the issue of social science's relation to philosophy and examining Bouzanis' concept of social imaginary as an imagery, raising questions of sense. [ABSTRACT FROM AUTHOR]

This article looked at how the ‘Ghanaian Times’ newspaper, using stories sourced from the British Broadcasting Corporation (BBC), framed Africa to the rest of the world. The study which utilized the mixed approach also employed the gatekeeping, framing and cultural imperialist theories in its attempt to find out which of the regional blocs in Africa dominated the coverage and whether the portrayal was positive or negative. The study found that the majority of the stories that the ‘Ghanaian Times’ sourced from the BBC were negative about Africa hence framing Africa as an unhealthy place for living. Out of the 154 stories the newspaper sourced from the BBC, only 52 stories focused on the positive happenings in Africa while 94 stories painted a catastrophic image about Africa. Also, political unrest and crime were the two dominant themes Africa was associated with. In all, the general image of Africa as portrayed by the newspaper was negative as the editors focused more on negative stories of Africa compared to the positives amidst the abundance of stories published by the BBC. This arguably means that Africa’s negative image could only be corrected if editors are deliberate in selecting more positive stories about Africa for publication. This work will indeed add to the literature on the framing of Africa in the media especially the contribution of the African media to the dominant negative image Africa continues to be associated with.

ardiovascular imaging has become the foundation of heart failure diagnostic studies. The most crucial technique for clinically diagnosing cardiac diseases is echocardiography. Depending on the positioning and angles of the probe, different cardiac views can be obtained during echocardiography. Therefore, the automatic classification of echo views, especially for computer systems and even automatic diagnosis in later stages, is the first step for echocardiogram diagnosis. In addition, the classification of heart views allows the tagging of echo videos to be done on a high scale and the possibility of database management and collection is provided. However, deep learning is an advanced machine learning method that is used to analyze both natural and medical images. But so far, it has not been widely used on cardiac ultrasound, the reason is the complexity of formats with multiple views and multi-view formats of echocardiogram. The proposed topic of this research is to provide novel and effective architectures for cardiac view classification. The aim of this study is to overcome the challenges in processing, categorizing and recognizing echo views stored as videos and images. In particular, in order to extract features, automatic methods and deep networks have replaced manual methods. In the presented solution, by using the transfer learning and the 3d-cnn method in image and video classification, we have improved the accuracy of echo views classification. [ABSTRACT FROM AUTHOR]

During national crises, political elites often rally around the flag, promoting a central message to restore unity and calm the public. COVID-19 provided such a crisis. But did elites rally? The pandemic occurred at a point of extreme polarization in the United States, which threatens the potential for a rally. In this article, we argue that messaging about masking during COVID-19 offers an opportunity to test the comparative effects of a rally versus polarization. To do so, we use a unique measure: visual public communication by members of Congress (MOCs). We extract 340,000 images from congressional Twitter and Facebook accounts and employ supervised machine-learning methods to identify when MOCs posted images of people wearing masks. We find evidence of both rally effects and polarization. Trump's actions are especially important: while Trump-loyal Republicans are less likely to post masks, all Republicans increased posting masks after Trump first appeared wearing a face mask. [ABSTRACT FROM AUTHOR]

Copyright of National Palace Museum Research Quarterly is the property of National Palace Museum and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper addresses the classification of images depicting the eruptive activity of Mount Etna, captured by a network of ground-based thermal cameras. The proposed approach utilizes Convolutional Neural Networks (CNNs), focusing on pretrained models. Eight popular pretrained neural networks underwent systematic evaluation, revealing their effectiveness in addressing the classification problem. The experimental results demonstrated that, following a retraining phase with a limited dataset, specific networks such as VGG-16 and AlexNet, achieved an impressive total accuracy of approximately 90%. Notably, VGG-16 and AlexNet emerged as practical choices, exhibiting individual class accuracies exceeding 90%. The case study emphasized the pivotal role of transfer learning, as attempts to solve the classification problem without pretrained networks resulted in unsatisfactory outcomes.

This research aimed to study the changing Liannichang’s images in The Bride with White Hair, a Swordman novel published in 1996, and The Bride with White Hair TV series in 2012. The theory of adaptation was used for analysis. The result showed that Liannichang’s images in the novel have brutal, helpful, sympathetic, grateful, and faithful images. Liannichang’s images in the TV series have brutal, helpful, sacrificeable, and faithful images. The researcher found that Liannichang’s family background persisted throughout in that she did not have a family and grew up with a wolf. When adapted as a TV series, it was revealed that Liannichang had symptoms of sickness (she did not control herself). This was reflected in a brutal image. Character relationships have conventions, extensions, reductions, and modifications in some situations, and these factors reflect helpful, sacrificeable, grateful, and faithful images. When adapted to the TV series, these reduced sympathetic and grateful images, but appeared instead to be sacrificeable because the TV series modified Tiefeilong in the negative role. These images still reflect the five constant virtues of Confucianism; they showed that the helpful and sympathetic images reflected benevolence, and they can expand the grateful image of propriety. The faithful image reflects fidelity. The last factors that reflect on the changing Liannichang’s images are the era and the limitations of the story