35 research outputs found

    Esophageal cancer presenting with atrial fibrillation: A case report

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    <p>Abstract</p> <p>Introduction</p> <p>Atrial fibrillation was previously reported in patients with esophageal cancer as a complication of total esophagectomy or photodynamic therapy. Here, we propose that atrial fibrillation may also be caused by external compression of the left atrium by esophageal cancer.</p> <p>Case presentation</p> <p>We present a 58-year-old man who developed atrial fibrillation with rapid ventricular rate in the emergency room while being evaluated for dysphagia and weight loss. Atrial fibrillation lasted less than 12 hours and did not recur. Echocardiogram did not reveal any structural heart disease. A 10-cm, ulcerated mid-esophageal mass was seen during esophagogastroscopy. Microscopic examination showed squamous cell carcinoma. Computed tomography of the chest revealed esophageal thickening compressing the left atrium.</p> <p>Conclusion</p> <p>External compression of the left atrium was previously reported to provoke atrial fibrillation. Similarly, esophageal cancer may precipitate atrial fibrillation by mechanical compression of the left atrium or pulmonary veins, triggering ectopic beats in susceptible patients.</p

    A Comprehensive assessment of Convolutional Neural Networks for skin and oral cancer detection using medical images

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    Early detection is essential to effectively treat two of the most prevalent cancers, skin and oral. Deep learning approaches have demonstrated promising results in effectively detecting these cancers using Computer-Aided Cancer Detection (CAD) and medical imagery. This study proposes a deep learning-based method for detecting skin and oral cancer using medical images. We discuss various Convolutional Neural Network (CNN) models such as AlexNet, VGGNet, Inception, ResNet, DenseNet, and Graph Neural Network (GNN). Image processing techniques such as image resizing and image filtering are applied to skin cancer and oral cancer images to improve the quality and remove noise from images. Data augmentation techniques are used next to expand the training dataset and strengthen the robustness of the CNN model. The best CNN model is selected based on the training accuracy, training loss, validation accuracy, and validation loss. The study shows DenseNet achieves state-of-the-art performance on the skin cancer dataset

    Facial Expression Recognition ā€“ A Study and its counterparts

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    Ā Ā Facial Expression Recognition ā€“ A Study and its counterparts Ā Ā </p
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