Background:
Masses are one of the most important indicators of breast cancer in mammograms,
and their classification into two groups as benign and malignant is highly necessary.
Computer Aided Diagnosis (CADx) helps radiologists enhance the accuracy of their decision. Hence, the system is required to support and assess with radiologist's interaction as an expert.
Methods:
In this research, classification of breast masses using mammography in the two main
views which include MLO and CC, is evaluated with respect to the shape, texture and asymmetry
aspect. Additionally, a method was developed and proposed using the classification of breast tissue
density based on the decision tree.
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Discussion: This study therefore, aims to provide a method based on the human decision-making
model that will help in designing the perfect tool for radiologists, regardless of the complexity of
computing, costly procedures and also reducing the diagnosis error.
Conclusion:
Results show that the proposed system for entirely fat, scattered fibroglandular densities,
heterogeneously dense, and extremely dense breast achieved 100, 99, 99 and 98% true malignant
rate, respectively with cross-validation procedure.
Nowadays, telemedicine can provide remote clinical services for the elderly, using smart devices like embedded sensors, via real-time communication with the healthcare provider. In particular, inertial measurement sensors such as accelerometers embedded in smartphones can provide sensory data fusion for human activities. Thus, the technology of Human Activity Recognition can be applied to handle such data. In recent studies, the three-dimensional axis has been used to detect human activities. Since most changes in individual activities occur in the x- and y-axis, the label of each activity is determined using a new two-dimensional Hidden Markov Mode based on these two axes. To evaluate the proposed method, we use the WISDM dataset which is based on an accelerometer. The proposed strategy is compared to General Model and User-Adaptive Model. The results indicate that the proposed model is more accurate than the others.
Although various clinical factors affect the diagnosis of Non-alcoholic Fatty Liver Disease (NAFLD), most studies only use single-source data such as images or laboratory data. Nevertheless, using different categories of features can help to get better results. Hence, one of the most important purposes of this paper is to employ a multi-group of effective factors such as velocimetry, psychological, demographic and anthropometric, and lab test data. Then, some Machine Learning (ML) methods are applied to classify the samples into two healthy and patient with NAFLD groups. The data used here belongs to the PERSIAN Organizational Cohort study at Mashhad University of Medical Sciences. To quantify the scalability of the models, different validity metrics are used. The obtained results illustrate that the proposed method can lead to an increase in the efficiency of the classifiers.
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