A blood count is one of the most important diagnostic tools in medicine and one of the most common procedures. It can reveal important changes in the body and is commonly used as the first stage in the process of evaluating patients’ health. Even though this is a common practice, delivering examinations in laboratories can be difficult due to the availability of expensive technology that requires frequent maintenance. This study is developing an alternative deep learning computational model capable of automatically detecting cells in images of blood samples. Using object detection libraries, it was possible to train a model that was focused on this task and capable of detecting cells in images with adequate accuracy. When the identification of cells in images of blood samples was taken into account in the best results obtained, it was possible to count white cells with an accuracy of one hundred percent, red cells with an accuracy of 89%, and platelets with an accuracy of 96%, which generated subsidies to develop the primary components of a blood count. The components that were supposed to classify the various types of white cells were not carried out due to the limits of the dataset provided. On the other hand, the study can be broadened to include further works that deal with this issue because it produced satisfactory results.
Pneumonia is a disease that spreads quickly and poses a serious risk to the health and well-being of its victims. An accurate biomedical diagnosis of pneumonia necessitates the use of various diagnostic tools and the evaluation of various clinical features, all of which are hindered by the lack of available experts and tools. According to the research presented here, a mobile app that uses deep learning techniques to classify whether or not a patient has pneumonia is being developed. It was hoped that a mobile application prototype for detecting pneumonia using neural networks would be developed as part of this study. The use of a high-level tool such as Create ML makes this process easier and eliminates issues such as how many layers a neural network has, initializing the model parameters, or which algorithms to use. The model can now be accessed by anyone, anywhere, via a mobile application. The dataset of more than 5,000 real images was used to train an image classification model using Create ML, a tool with a graphical interface, and there was no need for specialized knowledge.
Hyper arterial pressure (HAP) is a disease that kills silently because it does not produce symptoms in the early stages, making it difficult to diagnose. When it is detected, its treatment is not accessible to everyone, which affects the disease’s long-term development. Hypertension affects a large portion of the Iraqi population. In the current research paper, we have discussed how data mining can be applied to identify the status of the risk factors that affect arterial hypertension due to I10-I15 causes, evaluating the context variables disability, overwork, high-risk pregnancy, stress, high diets, and poor nutrition in the population between 50 and 64 years in the city of Baghdad. It is possible to see how data mining in large volumes of health data can generate new knowledge and thus uncover hidden patterns in the data through the development of this research. Attributes directly linked to disease prevalence can be found in data from Baghdad, Iraq, even if they are not directly linked to a specific cause. This shows that some variables are transversal to the development of the disease regardless of its categorization. Cluster analysis revealed that, even though these diseases are categorized as having different causes, they have a degree of incorrect classification of 40.71% because they present attributes with a similar behavior transversal to the disease and not the disease-specific cause for which it is categorized.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.