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.

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.

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.

Segmentation of breast tumors with more accuracy using computerized methods is essential for breast cancer monitoring and quantification. Both segmentation and classification of breast tumors using a fully automated or Computer-Aided Diagnosis system poses various problems in terms of imaging properties. In this work, a new hybrid algorithm is proposed for segmentation with a two-step process. Initially, a watershed transformation is applied to separate all basins based on pixel density variation from the mass present in tumors, since it has been quite booming in the presence of tumors in all circumstances. Though this is very perceptive to tiny fluctuations in the size of the image, large numbers of areas are produced unacceptably, and the boundaries after segmentations are also quite hard. The second level set is an effective method of segmenting all types of medical images because; it easily flows with, cavities, folds, splits, and merges. To make the recognition step easier and more accurate, the result of segmentation is considered the beginning position of the curve, and the same will be used at the next step of the level set. This produces a closed, smooth, and accurately placed contour or surface. As a result, the present research uses watershed segmentation to isolate tumor regions and performs classification using Feed Forward Neural Network (FNN) to extract features for classification. Experimental results are evaluated based on performance and quality analysis. In the classification process, the study obtained an accuracy rate of 91.2% in the learning model and 71.8% in a testing model.

Facial expression recognition has been more essential in artificial machine intelligence systems in recent years. Recognizing facial expressions automatically has constantly been considered as a challenging task since people significantly vary the way of exhibiting their facial expressions. Numerous researchers established diverse approaches to analyze the facial expressions automatically but there arise few imprecision issues during facial recognition. To address such shortcomings, our proposed approach recognizes the facial expressions of humans in an effective manner. The suggested method is divided into three stages: pre-processing, feature extraction, and classification. The inputs are pre-processed at the initial stage and CNN-BO algorithm is used to extract the best feature in the feature extraction step. Then the extracted feature is provided to the classification stage where MNN-SR algorithm is employed in classifying the face expression as joyful, miserable, normal, annoyance, astonished and frightened. Also, the parameters are tuned effectively to obtain high recognition accuracy. In addition to this, the performances of the proposed approach are computed by employing three various datasets namely; CMU/VASC, Caltech faces 1999, JAFFE and XM₂VTS. The performance of the proposed system is calculated and comparative analysis is made with few other existing approaches and its concluded that the proposed method provides superior performance with optimal recognition rate.

Markerless Augmented Reality (MAR) is a superior technology that is currently used by the medical device assembler with aid in design, assembly, disassembly and maintenance operations. The medical assembler assembles the medical equipment based on the doctors requirement, they also maintains quality and sanitation of the equipment. The major research challenges in MAR are as follows: establish automatic registration parts, find and track the orientation of parts, and lack of depth and visual features. This work proposes a rapid dual feature tracking method i.e., combination of Visual Simultaneous Localization and Mapping (SLAM) and Matched Pairs Selection (MAPSEL). The main idea of this work is to attain high tracking accuracy using the combined method. To get a good depth image map, a Graph-Based Joint Bilateral with Sharpening Filter (GRB-JBF with SF) is proposed since depth images are noisy due to the dynamic change of environmental factors that affects tracking accuracy. Then, the best feature points are obtained for matching using Oriented Fast and Rotated Brief (ORB) as a feature detector, Fast Retina Key point with Histogram of Gradients (FREAK-HoG) as a feature descriptor, and Feature Matching using Rajsk’s distance. Finally, the virtual object is rendered based on 3D affine and projection transformation. This work computes the performance in terms of tracking accuracy, tracking time, and rotation error for different distances using MATLAB R2017b. From the observed results, it is perceived that the proposed method attained the least position error value about 0.1 cm to 0.3 cm. Also, rotation error is observed as minimal between 2.40 (Deg) to 3.10 and its average scale is observed as 2.7140. Further, the proposed combination consumes less time against frames compared with other combinations and obtained a higher tracking accuracy of about 95.14% for 180 tracked points. The witnessed outcomes from the proposed scheme display superior performance compared with existing methods.

Cardiovascular disease (CVD) is most dreadful disease that results in fatal-threats like heart attacks. Accurate disease prediction is very essential and machine-learning techniques contribute a major part in predicting occurrence. In this paper, a novel machine learning based model for accurate prediction of cardiovascular disease is developed that applies unique feature selection technique called Chronic Fatigue Syndrome Best Known Method (CFSBKM). Each feature is ranked based on the feature importance scores. The new learning model eliminates the most irrelevant and low importance features from the datasets thereby resulting in the robust heart disease risk prediction model. The multi-nominal Naive Bayes classifier is used for the classification. The performance of the CFSBKM model is evaluated using the Benchmark dataset Cleveland dataset from UCI repository and the proposed models out-perform the existing techniques.

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.

In practical radiology, early diagnosis and precise categorization of liver cancer are difficult issues. Manual segmentation is also a time-consuming process. So, utilizing various methodologies based on an embedded system, we detect liver cancer from abdominal CT images using automated liver cancer segmentation and classification. The objective is to categorize CT scan images of primary and secondary liver disease using a Back Propagation Neural Network (BPNN) classifier, which has greater accuracy than previous approaches. In this work, a newly proposed method is shown which has four phases: image preprocessing, image segmentation, extraction of the features, and classification of the liver. Level set segmentation for segmenting the liver from abdominal CT images and Practical Swarm Optimization (PSO) for the tumor segmentation. Then the features from the liver are extracted and given to the BPNN classifier to classify the liver cancer. These algorithms are implemented on the Raspberry Pi. Then it serially interfaces with the MAX3232 protocol via serial communication. The GSM 800C module is connected to the system to send SMS as primary or secondary cancer. The BPNN classification technique achieved an excellent accuracy of 97.98%. The experimental results demonstrate the efficiency of this proposed approach, which provides excellent accuracy with good results.

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.