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https://www.selleckchem.com/products/blasticidin-s-hcl.html 1%) among the non-Ob/Gyn applicants. Ob/Gyn applicants and non-Ob/Gyn applicants differed in their assessment of Ob/Gyn rotations. It is crucial to provide medical training based on interns' needs to improve their skills for treating female patients. Ob/Gyn applicants and non-Ob/Gyn applicants differed in their assessment of Ob/Gyn rotations. It is crucial to provide medical training based on interns' needs to improve their skills for treating female patients.Females are approximately half of the total population worldwide, and most of them are victims of breast cancer (BC). Computer-aided diagnosis (CAD) frameworks can help radiologists to find breast density (BD), which further helps in BC detection precisely. This research detects BD automatically using mammogram images based on Internet of Medical Things (IoMT) supported devices. Two pretrained deep convolutional neural network models called DenseNet201 and ResNet50 were applied through a transfer learning approach. A total of 322 mammogram images containing 106 fatty, 112 dense, and 104 glandular cases were obtained from the Mammogram Image Analysis Society dataset. The pruning out irrelevant regions and enhancing target regions is performed in preprocessing. The overall classification accuracy of the BD task is performed and accomplished 90.47% through DensNet201 model. Such a framework is beneficial in identifying BD more rapidly to assist radiologists and patients without delay.Measuring the image focus is an important issue in Shape from Focus methods. Conventionally, the Sum of Modified Laplacian, Gray Level Variance (GLV), and Tenengrad techniques have been used frequently among various focus measure operators for estimating the focus levels in a sequence of images. However, they have various issues such as fixed window size and suboptimal focus quality. To solve these problems, a new focus measure operator based on the adaptive sum of weighted m

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