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Diagnosing a disease requires observing the affected tissues and drawing conclusions based on specific known features. Conventionally, a pathologist would diagnose the sample manually by placing it on a glass slide and viewing it under the microscope. These microscopes existed 400 years ago, but over the years, there have been modifications aimed at digitizing every possible diagnostic test. One of the major advantages of digitizing the process is the reduced time consumption for acquiring, processing, and analyzing the slides. Another positive aspect is the reduction in subjectivity achieved by utilizing artificial intelligence (AI) algorithms to classify and diagnose specific diseases. This is achieved by attaching a digital camera to the microscope, which captures images of the glass slides for subsequent processing and diagnosis. There has been a lot of research in this field, but its implementation has been hindered by challenges such as interoperability and high-resolution data, resulting in large file sizes. Various applications for whole slide imaging, such as disease diagnosis techniques, whole slide imaging (WSI) scanners, digital slide scanners, the Internet of Things (IoT), and AI, have been explored in this study. This paper reviews the trends and evolution of microscopes leading to present-day digital pathology scanners, with a major focus on one of the digital techniques, which is whole slide imaging. It also explores various areas where AI has been integrated into whole-slide imaging. [ABSTRACT FROM AUTHOR]

Monkeypox (Mpox) resurfaced in January 2022 as a rare zoonotic disease that spreads to many countries. Though the virus is not as dangerous as COVID-19, it has still caused many fatalities worldwide. The Mpox virus spreads when people are in close contact with infected individuals. Among many symptoms, the disease also causes skin rashes, and medical imaging can be used to diagnose the virus successfully. However, other diseases such as smallpox, chickenpox, and measles also cause similar skin rashes. Hence, artificial intelligence (AI) and machine learning (ML) can be highly beneficial in diagnosing Mpox from other similar diseases. After extensive model training, it is advantageous to use a standard camera to capture skin images of an infected patient and run it against deep learning (DL) models. In this research, we have used transfer learning models such as residual networks and SqueezeNet to diagnose Mpox from measles, chickenpox and healthy patients. An average accuracy of 91.19% and an F1-score of 92.55% were obtained for the Mpox class. The findings show that the models can be useful in detecting the contagious virus. Since the classifiers are easily deployable, they can be used on camera-ready devices such as phones and laptops. [ABSTRACT FROM AUTHOR]

Dengue is a vector‐borne disease that is highly endemic in countries located in tropical regions. It can cause severe complications and can even lead to death in the case of delayed diagnosis. Detection of dengue is done by manually examining the platelets and lymphocytes in Leishman's stained peripheral blood smear (PBS) images. PBS examination is considered the gold standard for diagnosing various haematological disorders. However, manual analysis of the PBS is labour‐intensive, tedious, and time‐consuming, requiring a skilled and experienced haematologist. Today, soft computing methods and artificial intelligence have made their way into every science and technology branch. One such area which has adopted this approach is digital pathology, for automatically identifying and diagnosing various diseases. The main objective of this work was to design an intelligent algorithm to classify normal and dengue patients with the help of digital microscopic blood smear images. A total of 94 normal and dengue‐infected PBSs were acquired at a magnification of 100×. Grey‐level segmentation based on Otsu's thresholding was used for the segmentation of the nucleus of lymphocytes. Distinct features from the nucleus that differentiated infected cells from normal were extracted using a pre‐trained MobileNetV2 network and local binary pattern. Significant features were selected using the ReliefF algorithm. Subsequently, these features were fed to the support vector machine (SVM) classifier. Our proposed system gave an accuracy, sensitivity, and specificity of 95.74%, 98.14%, and 92.50%, respectively. Hence, the developed intelligent model with deep and hand‐crafted features can be valuable for dengue diagnosis. [ABSTRACT FROM AUTHOR]