The nasal septum repair surgery is the dangerous operations that any stimulation of this area causes a large change in the rhythm of the heartbeat and blood pressure. This study aimed to determine the effects of tetracaine 0.5% on changes in heartbeat and heart rhythm, hemodynamic changes during surgery, intraoperative bleeding, and pain after septoplasty surgery. The irregular double-blind clinical trial registry of clinical trials Iran with the code number (IRCT: 20150526625N8) in the first half of 2013 on 86 patients in Kashani hospital of Shahrekord. Having selected and matched the patients were divided into two groups. Case group was dropped tetracaine 0.5% in each of the nasal cavity 15 min before the beginning of the operation. The control group was dropped distilled water 15 min preoperation in each of the nasal cavity. The surgery lasted about 30–60 min. Clinical symptoms were evaluated after anesthetic induction as well as pain using the visual analog scale after the operation, in the recovery room. The collected data were analyzed using SPSS version software 17 through independent t-test, Chi-square, and repeated measures variance analysis. Postoperative pain intensity in the experimental group compared to the control group was significantly lower than the control group (P < 0.05); however, blood pressure and heart rate during anesthesia, there was no difference between groups (P > 0.05). Based on the findings, intake of tetracaine drop 0.5% has no impact on some hemodynamic changes during septoplasty operation. However, compared with the control group, pain was significantly reduced.
In this research, a new method for automatic detection and classification of suspected breast cancer lesions using ultrasound images is proposed. In this fully automated method, de-noising using fuzzy logic and correlation among ultrasound images taken from different angles is used. Feature selection using combination of sequential backward search, sequential forward search and distance-based methods is obtained. A new segmentation method based on automatic selection of seed points and region growing is proposed and classification of lesions into two malignant and benign classes using combination of AdaBoost, Artificial Neural Network and Fuzzy Support Vector Machine classifiers and majority voting is implemented.
We present a new method for automatic detection of suspicious breast cancer lesions using ultrasound. The system is fully automated. It uses fuzzy logic and compounding for de-noising. A fuzzy membership function based on the gray values of ultrasound images is applied for de-noising, improving the quality of the image and increasing separation between foreground and background, thus making easier detection of lesions. A novel approach based on neural network is used for segmentation of ultrasound images, and correlation between ultrasound images taken from different angles allows overcoming the problem of shadowing. We consider a combination of morphological and texture features and use sequential forward search, sequential backward search, and distance-based method to select the best subset of features. We rank the features using distance-based method and use a combination of sequential forward search and sequential backward search to select the best features (bidirectional search). Finally, support vector machine classifier is used for detecting suspicious lesions. The results of experiments show that our system performs better than other state-of-the-art computer-aided diagnosis systems with the accuracy of 98.75%. Furthermore, we used concurrency to improve the computational efficiency. In concurrent implementation of de-noising, segmentation, and feature selection and extraction, we assign each pixel of an ultrasound image to a different thread. We also benefit from multi-core computing by running each classifier on a different thread. Concurrent implementation of our computer-aided diagnosis system reduces overall computational time by 85%.
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