This study aimed to develop a method for pulmonary artery and vein (PA/PV) separation in three-dimensional computed tomography (3DCT), using a dual reconstruction technique and the addition of CT images. The physical image properties of multiple reconstruction kernels (FC13; FC13 3D-Q03; FC30 3D-Q03; FC83; FC13 twofold addition; FC13 threefold addition; FC13 fourfold addition; FC13 [3D-Q03] twofold addition; FC13+FC30 (3D-Q03); FC13+FC83) were evaluated based on spatial resolution using a modulation transfer function. The lung kernel CT image (FC 83) had a high spatial resolution with a 10% modulation transfer function (0.847). The noise power spectrum of the additive CT images was measured, and the CT values for the PA/PV with and without addition were compared. The addition of CT images increased the CT values difference between the PA/PV. The PA/PV 3DCT angiography (PA/PV 3DCTA), even with a small difference in CT values, could be effectively separated using high spatial resolution kernel CT and the addition of CT images dedicated to subtraction. This novel, simple method could create PA/PV 3DCTA using a general CT scanner and 3D workstation that can be easily performed at any facility.
Rhino manometry is widely used for measuring nasal aerodynamic resistance, but its clinical use is still limited and needs further standardization. The main aim of the study is to determine the total nasal resistance and to locate the place and the reasons for nasal cavity obstruction. A scheme of modern hybrid computed Rhino manometry for functional diagnosis of upper respiratory disease is proposed. The role of the main parameters in nasal aerodynamics is described (the airflow, the pressure, different types of local resistances, and nasal respiratory energy efficiency). The hybrid approach is based on a CT study and the Rhino manometry data. The study discovers four new features in the nasal breathing graph, which help in discrimination between different types of breathing modes, and this increases the accuracy of calculating the pressure losses by 12%. Also, the method used for calculating the mucosal roughness, which used as a criterion for evaluating the airflow mode. The accuracy of the hybrid functional method in calculating the total nasal aerodynamic resistance is 30% higher than with previous methods.
Diabetes causes damage to the retinal blood vessel networks, resulting in Diabetic Retinopathy (DR). This is a serious vision-threatening condition for most diabetics. Color fundus photographs are utilized to diagnose DR, which necessitates the employment of qualified clinicians to detect the presence of lesions. It is difficult to identify DR in an automated method. Feature extraction is quite important in terms of automated sickness detection. Convolutional Neural Network (CNN) exceeds previous handcrafted feature-based image classification algorithms in terms of picture classification efficiency in the current environment. In order to improve classification accuracy, this work presents the CNN structure for extracting attributes from retinal fundus images. The output properties of CNN are given as input to different machine learning classifiers in this recommended strategy. This approach is evaluating using pictures from the EYEPACS datasets using Decision stump, J48 and Random Forest classifiers. To determine the effectiveness of a classifier, its accuracy, false positive rate (FPR), True positive Rate (TPR), precision, recall, F-measure, and Kappa-score are illustrated. The recommended feature extraction strategy paired with the Random forest classifier outperforms all other classifiers on the EYEPACS datasets, with average accuracy and Kappa-score (k-score) of 99% and 0.98 respectively.
In this paper, a time series data mining models is introduced for analysis of ECG data for prior identification of heart attacks. The ECG data sets extracted from Physionet are simulated in MATLAB. The Data used for model are preprocessed so that missing data are fulfilled. In this work cascade feedforward NN which is similar to Multilayer Perceptron (MLP) architecture is proposed along with Swarm Intelligence. A hybrid method combining cascade-Forward NN Classifier and Ant colony optimization is proposed in this paper. The swarm-based intelligence method optimizes the weight adjustment of neural network and enhances the convergence behavior. The novelty is on the optimization of the NN parameters for narrowing down the convergence with ACO implementation. Ant colony optimization is used here for choosing the optimized hidden node. The combined use of machine learning algorithm with neural network enhances the performance of the system. The performance is evaluated using parameters like True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN) respectively. The Improved accuracy of proposed Classifier model raises the speed. In addition, the proposed method uses minimum memory. The implementation was done in MATLAB tool. Real time data was used.
Brain Tumour is a one of the most threatful disease in the world. It reduces the life span of human beings. Computer vision is advantageous for human health research because it eliminates the need for human judgement to get accurate data. The most reliable and secure imaging techniques for magnetic resonance imaging are CT scans, X-rays, and MRI scans (MRI). MRI can locate tiny objects. The focus of our paper will be the many techniques for detecting brain cancer using brain MRI. Early detection of tumour and diagnosis is might essential to radiologist to initiate better treatment. MRI is a competent and speedy method of examining a brain tumour. Resonance in Magnetic Fields Imaging technology is a non-invasive technique that aids in the segmentation of brain tumour images. Deep learning algorithm delivers good outcomes in terms of reducing time consumption and precise tumour diagnosis (solution). This research proposed that a Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) Supervised Deep Learning model be used to automatically find and split brain tumours. The RNN Model outperforms the CNN Model by 98.91 percentage. These models categorize brain images as normal or pathological, and their performance was evaluated.
