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Smegma pearl (or smegmoma) is a benign condition encountered in young boys. It can be a cause for parental concern. The condition is usually self-limiting and asymptomatic. There are very few cases reported in the literature, although this condition is not rare. We report the condition in three toddlers who presented with yellowish-white papulonodular lesions on the glans and preputial region along with partial adhesions of the undersurface of the prepuce to the glans. This report aims at creating awareness about this condition, to avoid unnecessary diagnostic workups and apprehensions.

Objectives Pemphigus, a group of autoimmune bullous diseases, can be fatal, resulting from overwhelming opportunistic infection of lesions secondary to cutaneous bacterial infections. This study aimed to look into the cutaneous bacterial infection profile of pemphigus patients as timely identification and appropriate treatment can play a major role in reducing mortality.

Hand gesture recognition is critical to human-computer interaction. This paper proposes a revolutionary permanent method for hand gestures and presents a framework for detecting rapid gestures using information combination techniques and a direct indicator of hand development. The foundation subtraction approach in this system removes the arm area from the foundation. This structure is currently adopted by the procedures for implementing the Kinect V2 application. The required time is confirmed quickly compared to other ongoing minutes. The time analysis is compared and using the data pooling approach, the average time is 63ms. The average time is 45 milliseconds when using Rapid Data Integration. The time it takes to recognize hand movement has been reduced. MATLAB is used to analyze the results of experiments. [ABSTRACT FROM AUTHOR]

Measurement(s) glaucoma severity grading Technology Type(s) cup-to-disc ratio Factor Type(s) Indian ethnicity fundus image database for glaucoma prescreening Sample Characteristic - Organism Human Sample Characteristic - Environment Ophthalmology Sample Characteristic - Location India

Retinal volume computation is one of the critical steps in grading pathologies and evaluating the response to a treatment. We propose a deep-learning-based visualization tool to calculate the fluid volume in retinal optical coherence tomography (OCT) images. The pathologies under consideration are Intraretinal Fluid (IRF), Subretinal Fluid (SRF), and Pigmented Epithelial Detachment (PED). We develop a binary classification model for each of these pathologies using the Inception-ResNet-v2 and the small Inception-ResNet-v2 models. For visualization, we use several standard Class Activation Mapping (CAM) techniques, namely Grad-CAM, Grad-CAM++, Score-CAM, Ablation-CAM, and Self-Matching CAM, to visualize the pathology-specific regions in the image and develop a novel Ensemble-CAM visualization technique for robust visualization of OCT images. In addition, we demonstrate a Graphical User Interface that takes the visualization heat maps as the input and calculates the fluid volume in the OCT C-scans. The volume is computed using both the region-growing algorithm and selective thresholding technique and compared with the ground-truth volume based on expert annotation. We compare the results obtained using the standard Inception-ResNet-v2 model with a small Inception-ResNet-v2 model, which has half the number of trainable parameters compared with the original model. This study shows the relevance and usefulness of deep-learning-based visualization techniques for reliable volumetric analysis.