Personal Identification Using Ears Based on Statistical Features

Advisor/s: Atalla I. Hashad, Gouda I. Salama. Date and location of PhD thesis defense: May 2014, AASTMTBiometrics is an automated method of recognizing a person based on a physiological (e.g. face, iris, or retina) or behavioral (e.g. gait, signature, or dynamic keystrokes) characteristics. Ear recognition is one of the physiological biometrics' types that have been interested in the recent years. Ear recognition, achieves good accuracy and has many advantages such as it doesn't affected by expressions, health, and more stable than many other biometrics. However, it has many challenges such as the pose of the face, lighting variation, occlusion with hair or clothes. In this research, four proposed models are used to identify people using ear images. The first model used single feature extraction method based on single classifier. While, the second model used single feature extraction method based on multi-classifiers. The third model used feature combination techniques (parallel or serial) based on single classifier. Finally, in the fourth model multi-features and multi-classifiers are used. In this research, there are four methods that are used to extract the features, namely, \textit{Principal Component Analysis} (PCA), \textit{Linear Discriminant Analysis} (LDA), \textit{Independent Component Analysis} (ICA), and \textit{Discrete Cousin Transform} (DCT). Neural networks, decision tree, and minimum distance classifiers are used to classify the unknown samples. The occlusion problem with hair or scarves is one of the big challenges of the ear recognition systems. In this research, segmentation technique is proposed to neglect the occluded part and solve the occlusion problem. The idea of the segmentation technique is based on dividing the ear images into different parts. The occluded part/s is neglected and the rest of the parts are used to identify people based on features fusion and classifiers fusion. The segmentation technique consists of two main types, namely, uniform or non-uniform segmentation techniques. In this research, the uniform segmentation technique is used for many experiments (horizontal, vertical, and grid). All the four proposed models are applied to all ear segments to investigate the power of each model and to achieve a high accuracy. In this research, ear database images is used. The ear dataset consists of 102 grayscale images (6 images for each of 17 subjects) in PGM format [1]. The proposed models are achieved good identification rates using ear images. In the first model, the best accuracy achieved using LDA and neural network classifier. The results of the first model ranged from 64.12\% to 100\%. In the second model, many classifiers are fused to increase the recognition rate. In this method, two methods are used, namely, Borda count and majority voting. The results of this model ranged from 94.12\% to 96.08\%. The third model, the features using two different methods, namely serial and parallel are combined. The results of this model prove that the serial combination is more powerful than parallel combination. Finally, in the fourth model, two features and two classifiers are fused to get one decision. The accuracy of this model is approximately the same of the third model, and it does not achieve good results because there is a diversity between different classifiers. Moreover, the proposed segmentation model achieved good results when some parts of the ear images are occluded

Characteristics and outcomes of COVID-19 patients admitted to hospital with and without respiratory symptoms

Background: COVID-19 is primarily known as a respiratory illness; however, many patients present to hospital without respiratory symptoms. The association between non-respiratory presentations of COVID-19 and outcomes remains unclear. We investigated risk factors and clinical outcomes in patients with no respiratory symptoms (NRS) and respiratory symptoms (RS) at hospital admission. Methods: This study describes clinical features, physiological parameters, and outcomes of hospitalised COVID-19 patients, stratified by the presence or absence of respiratory symptoms at hospital admission. RS patients had one or more of: cough, shortness of breath, sore throat, runny nose or wheezing; while NRS patients did not. Results: Of 178,640 patients in the study, 86.4 % presented with RS, while 13.6 % had NRS. NRS patients were older (median age: NRS: 74 vs RS: 65) and less likely to be admitted to the ICU (NRS: 36.7 % vs RS: 37.5 %). NRS patients had a higher crude in-hospital case-fatality ratio (NRS 41.1 % vs. RS 32.0 %), but a lower risk of death after adjusting for confounders (HR 0.88 [0.83-0.93]). Conclusion: Approximately one in seven COVID-19 patients presented at hospital admission without respiratory symptoms. These patients were older, had lower ICU admission rates, and had a lower risk of in-hospital mortality after adjusting for confounders

Global variation in postoperative mortality and complications after cancer surgery: a multicentre, prospective cohort study in 82 countries

© 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 licenseBackground: 80% of individuals with cancer will require a surgical procedure, yet little comparative data exist on early outcomes in low-income and middle-income countries (LMICs). We compared postoperative outcomes in breast, colorectal, and gastric cancer surgery in hospitals worldwide, focusing on the effect of disease stage and complications on postoperative mortality. Methods: This was a multicentre, international prospective cohort study of consecutive adult patients undergoing surgery for primary breast, colorectal, or gastric cancer requiring a skin incision done under general or neuraxial anaesthesia. The primary outcome was death or major complication within 30 days of surgery. Multilevel logistic regression determined relationships within three-level nested models of patients within hospitals and countries. Hospital-level infrastructure effects were explored with three-way mediation analyses. This study was registered with ClinicalTrials.gov, NCT03471494. Findings: Between April 1, 2018, and Jan 31, 2019, we enrolled 15 958 patients from 428 hospitals in 82 countries (high income 9106 patients, 31 countries; upper-middle income 2721 patients, 23 countries; or lower-middle income 4131 patients, 28 countries). Patients in LMICs presented with more advanced disease compared with patients in high-income countries. 30-day mortality was higher for gastric cancer in low-income or lower-middle-income countries (adjusted odds ratio 3·72, 95% CI 1·70–8·16) and for colorectal cancer in low-income or lower-middle-income countries (4·59, 2·39–8·80) and upper-middle-income countries (2·06, 1·11–3·83). No difference in 30-day mortality was seen in breast cancer. The proportion of patients who died after a major complication was greatest in low-income or lower-middle-income countries (6·15, 3·26–11·59) and upper-middle-income countries (3·89, 2·08–7·29). Postoperative death after complications was partly explained by patient factors (60%) and partly by hospital or country (40%). The absence of consistently available postoperative care facilities was associated with seven to 10 more deaths per 100 major complications in LMICs. Cancer stage alone explained little of the early variation in mortality or postoperative complications. Interpretation: Higher levels of mortality after cancer surgery in LMICs was not fully explained by later presentation of disease. The capacity to rescue patients from surgical complications is a tangible opportunity for meaningful intervention. Early death after cancer surgery might be reduced by policies focusing on strengthening perioperative care systems to detect and intervene in common complications. Funding: National Institute for Health Research Global Health Research Unit

Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

© 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licenseBackground: Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide. Methods: A multimethods analysis was performed as part of the GlobalSurg 3 study—a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital. Findings: Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3·85 [95% CI 2·58–5·75]; p<0·0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63·0% vs 82·7%; OR 0·35 [0·23–0·53]; p<0·0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer. Interpretation: Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised. Funding: National Institute for Health and Care Research