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In recent years, the number of studies using whole-slide imaging (WSIs) of histopathology slides has expanded significantly. For the development and validation of artificial intelligence (AI) systems, glass slides from retrospective cohorts including patient follow-up data have been digitized. It has become crucial to determine that the quality of such resources meets the minimum requirements for the development of AI in the future. The need for automated quality control is one of the obstacles preventing the clinical implementation of digital pathology work processes. As a consequence of the inaccuracy of scanners in determining the focus of the image, the resulting visual blur can render the scanned slide useless. Moreover, when scanned at a resolution of 20× or higher, the resulting picture size of a scanned slide is often enormous. Therefore, for digital pathology to be clinically relevant, computational algorithms must be used to rapidly and reliably measure the picture’s focus quality and decide if an image requires re-scanning. We propose a metric for evaluating the quality of digital pathology images that uses a sum of even-derivative filter bases to generate a human visual-system-like kernel, which is described as the inverse of the lens’ point spread function. This kernel is then used for a digital pathology image to change high-frequency image data degraded by the scanner’s optics and assess the patch-level focus quality. Through several studies, we demonstrate that our technique correlates with ground-truth z-level data better than previous methods, and is computationally efficient. Using deep learning techniques, our suggested system is able to identify positive and negative cancer cells in images. We further expand our technique to create a local slide-level focus quality heatmap, which can be utilized for automated slide quality control, and we illustrate our method’s value in clinical scan quality control by comparing it to subjective slide quality ratings. The proposed method, GoogleNet, VGGNet, and ResNet had accuracy values of 98.5%, 94.5%, 94.00%, and 95.00% respectively.

Almost two million Muslim pilgrims from all around the globe visit Mecca each year to conduct Hajj. Each year, the number of pilgrims grows, creating worries about how to handle such large crowds and avoid unpleasant accidents or crowd congestion catastrophes. In this paper, we introduced deep Hajj crowd dilated convolutional neural network (DHCDCNNet) for crowd density analysis. This research also presents augmentation technique to create additional dataset based on the hajj pilgrimage scenario. We utilized a single framework to extract both high-level and low-level features. For creating additional dataset we divide the process of images augmentation into two routes. In the first route, we utilized magnitude extraction followed by the polar magnitude. In the second route, we performed morphological operation followed by transforming the image into skeleton. This paper presented a solution to the challenge of measuring crowd density using a surveillance camera pointed at a distance. An FCNN-based technique for crowd analysis is included in the proposed methodology, particularly for classifying crowd density. There are several obstacles in video analysis when there are a large number of pilgrims moving around the tawaf area, with densities of between 7 and 8 per square meter. The proposed DHCDCNNet method has achieved accuracy of 97%, 89% and 100% for the JHU-CROWD dataset, the UCSD dataset and the proposed Hajj-Crowd dataset, respectively. The proposed Hajj-Crowd dataset, the UCSD dataset, and the JHU-CROW dataset all had accuracy of 98%, 97% and 97%, respectively, using the VGGNet approach. Using the ResNet50 approach, the proposed Hajj-Crowd dataset, the UCSD dataset, and the JHU-CROW dataset all had an accuracy of 99%, 91% and 97%, respectively.