Current Issue : January-March Volume : 2023 Issue Number : 1 Articles : 5 Articles
The segmentation of brain tissue by MRI not only contributes to the study of the function and anatomical structure of the brain, but it also offers a theoretical foundation for the diagnosis and treatment of brain illnesses. When discussing the anatomy of the brain in a clinical setting, the terms “white matter,” “gray matter,” and “cerebrospinal fluid” are the ones most frequently used (CSF). However, due to the fact that the human brain is highly complicated in its structure and that there are many different types of brain tissues, the human brain structure of each individual has its own set of distinctive qualities. Because of these several circumstances, the process of segmenting brain tissue will be challenging. In this article, several different clustering algorithms will be discussed, and their performance and effects will be compared to one another. The goal of this comparison is to determine which algorithm is most suited for segmenting MRI brain tissue. Based on the clustering method, the primary emphasis of this research is placed on the segmentation approach that is appropriate for medical brain imaging. The qualitative and quantitative findings of the experiment reveal that the FCM algorithm has more steady performance and better universality, but it is necessary to include the additional auxiliary conditions in order to achieve more ideal outcomes....
Machine learning has already been used as a resource for disease detection and health care as a complementary tool to help with various daily health challenges. The advancement of deep learning techniques and a large amount of data-enabled algorithms to outperform medical teams in certain imaging tasks, such as pneumonia detection, skin cancer classification, hemorrhage detection, and arrhythmia detection. Automated diagnostics, which are enabled by images extracted from patient examinations, allow for interesting experiments to be conducted. This research differs from the related studies that were investigated in the experiment. These works are capable of binary categorization into two categories. COVID-Net, for example, was able to identify a positive case of COVID-19 or a healthy person with 93.3% accuracy. Another example is CHeXNet, which has a 95% accuracy rate in detecting cases of pneumonia or a healthy state in a patient. Experiments revealed that the current study was more effective than the previous studies in detecting a greater number of categories and with a higher percentage of accuracy. The results obtained during the model’s development were not only viable but also excellent, with an accuracy of nearly 96% when analyzing a chest X-ray with three possible diagnoses in the two experiments conducted....
Chest X-ray (CXR) characteristics of patients with drug-resistant tuberculosis (DR-TB) depend on a variety of factors, and therefore, identifying the influence of these factors on the appearance of DR-TB in chest X-rays can help physicians improve diagnosis and clinical suspicion. Our aim was to describe the CXR presentation of patients with DR-TB and its association with clinical and demographic factors. A retrospective analysis of the CXRs of DR-TB patients in Nigeria between 2010 and 2016 was performed, reviewing features of chest radiographs, such as cavitation, opacity and effusion, infiltration and lung destruction. The association of these abnormal CXR findings with clinical and demographic characteristics was evaluated using bivariate and multivariate models, and a p-value < 0.05 was considered statistically significant with a 95% confidence interval. A total of 2555 DR-TB patients were studied, the majority (66.9%) were male, aged 29–38 years (36.8%), previously treated (77%), from the South West treatment zone (43.5%), HIV negative (76.7%) and bacteriologically diagnosed (89%). X-ray findings were abnormal in 97% of the participants, with cavitation being the most common (41.5%). Cavitation, effusion, fibrosis, and infiltration were higher in patients presenting in the South West zone and in those previously treated for DR-TB, while lung destruction was significantly higher in patients who are from the South South zone, and in those previously treated for DR-TB. Patients from the South East zone (AOR: 6.667, 95% CI: 1.383–32.138, p = 0.018), the North East zone (AOR: 6.667, 95% CI: 1.179–37.682, p = 0.032) and the North West zone (AOR: 6.30, 95% CI: 1.332–29.787, p = 0.020) had a significantly increased likelihood of abnormal chest X-ray findings, and prior TB treatment predisposed the patient to an increased likelihood of abnormal chest X-ray findings compared to new patients (AOR: 8.256, 95% CI: 3.718–18.330, p = 0.001). The finding of a significantly higher incidence of cavities, effusions and fibrosis in DR-TB patients previously treated could indicate late detection or presentation with advanced DR-TB disease, which may require a more individualized regimen or surgical intervention....
The research of application models based on traditional convolutional neural networks has gradually entered the bottleneck period of performance improvement, and the improvement of chest X-ray image models has gradually become a difficult problem in the study. In this paper, the Swin Transformer is introduced into the application model of pneumonia recognition in chest X-ray images, and it is optimized according to the characteristics of chest X-ray images. The experimental results based on the model in this paper are compared with those of the model built with the traditional convolutional neural network as the backbone network, and the accuracy of the model is proved to be greatly improved. After the comparison experiments on two different datasets, the experimental results show that the accuracy of the model in this paper improves from 76.3% to 87.3% and from 92.8% to 97.2%, respectively. The experiments show that the accuracy of image enhancement based on the features of chest X-ray images in this model will be higher than the accuracy without image enhancement. In the experiments of this paper, the identification decision factors in the chest X-ray images were extracted by grad-cam combined with a transformer to find the corresponding approximate lesion regions....
The process of pneumonia detection has been the focus of researchers as it has proved itself to be one of the most dangerous and lifethreatening disorders. In recent years, many machine learning and deep learning algorithms have been applied in an attempt to automate this process but none of them has been successful significantly to achieve the highest possible accuracy. In a similar attempt, we propose an enhanced approach of a deep learning model called restricted Boltzmann machine (RBM) which is named enhanced RBM (ERBM). One of the major drawbacks associated with the standard format of RBM is its random weight initialization which leads to improper feature learning of the model during the training phase, resulting in poor performance of the machine. This problem has been tried to eliminate in this work by finding the differences between the means of a specific feature vector and the means of all features given as inputs to the machine. By performing this process, the reconstruction of the actual features is increased which ultimately reduces the error generated during the training phase of the model. The developed model has been applied to three different datasets of pneumonia diseases and the results have been compared with other state of the art techniques using different performance evaluation parameters. The proposed model gave highest accuracy of 98.56% followed by standard RBM, SVM, KNN, and decision tree which gave accuracies of 97.53%, 92.62%, 91.64%, and 88.77%, respectively, for dataset named dataset 2. Similarly, for the dataset 1, the highest accuracy of 96.66 has been observed for the eRBM followed by srRBM, KNN, decision tree, and SVM which gave accuracies of 90.22%, 89.34%, 87.65%, and 86.55%, respectively. In the same way, the accuracies observed for the dataset 3 by eRBM, standard RBM, KNN, decision tree, and SVM are 92.45%, 90.98%, 87.54%, 85.49%, and 84.54%, respectively. Similar observations can also be seen for other performance parameters showing the efficiency of the proposed model. As revealed in the results obtained, a significant improvement has been observed in the working of the RBM by introducing a new method of weight initialization during the training phase. The results show that the improved model outperforms other models in terms of different performance evaluation parameters, namely, accuracy, sensitivity, specificity, F1-score, and ROC curve....
Loading....