Automatic Detection of Optic Disc in Retinal Image by Using Keypoint Detection, Texture Analysis, and Visual Dictionary Techniques

With the advances in the computer field, methods and techniques in automatic image processing and analysis provide the opportunity to detect automatically the change and degeneration in retinal images. Localization of the optic disc is extremely important for determining the hard exudate lesions or neovascularization, which is the later phase of diabetic retinopathy, in computer aided eye disease diagnosis systems. Whereas optic disc detection is fairly an easy process in normal retinal images, detecting this region in the retinal image which is diabetic retinopathy disease may be difficult. Sometimes information related to optic disc and hard exudate information may be the same in terms of machine learning. We presented a novel approach for efficient and accurate localization of optic disc in retinal images having noise and other lesions. This approach is comprised of five main steps which are image processing, keypoint extraction, texture analysis, visual dictionary, and classifier techniques. We tested our proposed technique on 3 public datasets and obtained quantitative results. Experimental results show that an average optic disc detection accuracy of 94.38%, 95.00%, and 90.00% is achieved, respectively, on the following public datasets: DIARETDB1, DRIVE, and ROC

Computer-Aided Diagnosis of Parkinson's Disease Using Complex-Valued Neural Networks and mRMR Feature Selection Algorithm

ABSTRACT Parkinson's disease (PD) is a neurological disorder which has a significant social and economic impact. PD is diagnosed by clinical observation and evaluations, coupled with a PD rating scale. However, these methods may be insufficient, especially in the initial phase of the disease. The processes are tedious and time-consuming, and hence systems that can automatically offer a diagnosis are needed. In this study, a novel method for the diagnosis of PD is proposed. Biomedical sound measurements obtained from continuous phonation samples were used as attributes. First, a minimum redundancy maximum relevance (mRMR) attribute selection algorithm was applied for the identification of the effective attributes. After conversion to a complex number, the resulting attributes are presented as input data to the complex-valued artificial neural network (CVANN). The proposed novel system might be a powerful tool for effective diagnosis of PD

A new fuzzy logic based career guidance system: WEB-CGS

Izbor zanimanja na mnogo načina uvelike utječe na društveni život pojedinaca. Ipak, izbor odgovarajuće karijere postaje sve teži kad se uzme u obzir postojanje sve većeg broja zanimanja i mogućnosti usmjeravanja. Shodno tome, sve je veća važnost profesionalnog usmjeravanja. U ovom se radu razvija sustav pomoću kojega se automatski može ponuditi profesionalno usmjeravanje. To je WEB-CGS (web-based carrier guidance system) koji funkcionira kao web usluga koja se zasniva na neizrazitoj (fuzzy) logici. Cilj je olakšati pojedincu izbor odgovarajućeg zanimanja. U tom sustavu integrirani su prethodni uspjesi u obrazovanju učenika i mišljenja nastavnika te je omogućeno prepoznavanje profesionalnih interesa i mogućnosti učenika. Sustav predviđa interes učenika za usmjeravanje u području informacijske tehnologije, elektrike-elektronike, računovodstva i industrije automobila. Postignuti su obećavajući rezultati usmjeravanja za 300 neopredijeljenih studenata 9. razreda u strukovnoj srednjoj školi.Choosing a career affects individuals’ social life deeply in terms of many dimensions. However, choosing the right career is becoming increasingly difficult given the existence of an increasing number of professions and training opportunities. Consequently, the importance of career orientation increases. In this study, a system that can automatically offer vocational guidance has been developed. This new system is referred to as WEB-CGS (web-based carrier guidance system) and works as a fuzzy logic based web service. The aim is that it will make it easier for an individual to choose the right profession. In this system, students’ prior educational successes and teachers’ views were integrated in a manner which made it possible to identify the students’ professional interests and capacities. The system forecasts vocational school students’ interest with regard to Information Technology, Electrics-Electronics, Accounting, and Automotive. Promising results were obtained with regard to 300 unbiased 9th grade students in terms of orienting them towards an appropriate profession

Evaluation of COPD patient’s relatives assessment of disease awarness, load of care giving and loss of workforce: Turkish Thoracic Society COPD working group

Objective: Our aim is to measure the level of awareness of patient’s relatives COPD, to determine the caregiver burden of patient's relatives, and to determine whether there is a work day loss. Material and method: 252 COPD patients and 252 patient’s relatives from 11 centers were included in this questionnaire study. Ethics committee was approval. Disease information of the patients were recorded and a questionnaire was applied. Socio-demographic characteristics of the patient’s relatives were recorded and a questionnaire consisting of 24 questions including COPD disease, treatment and loss of working days and Zarit Scale used in chronic diseases were used. Results: 128(50.8%) of the patients according to GOLD were group-D.97(38.5%) of the patient's relatives were working. 253(94.4%) knew that COPD was a lung disease. 62(24.7%) were not able to go to work for 1-14 days. 125(57.1%) spent outside the home from 1 to 14 nights, because those accompanied to patients. In univariate analysis were detected mMRC(p<0.001), CAT(p<0.001), the number of comorbidities of patients(p=0.027),Conclusion: In COPD increases caregiving burden. This burden is greater in symptomatic patients and when comorbidities are present. Psycho-social and legal regulations should be investigated and solutions should be produced for the person who gives care to COPD patients

Cropped Quad-Tree Based Solid Object Colouring with Cuda

In this study, surfaces of solid objects are coloured with Cropped Quad-Tree method utilizing GPU computing optimization. There are numerous methods used in solid object colouring. When the studies carried out in different fields are taken into consideration, it is seen that quad-tree method displays a prominent position in terms of speed and performance. Cropped quad-tree is obtained as a result of the developments seen with the frequent use of this method in the field of computer sciences. Two different versions of algorithm which operate recursively on CPU and at the same time which use GPU computing optimization are used in this study. Besides, OpenGL is used for graphics drawing process. Within the setting of the study, results are obtained via CPU and GPU’s, at first using Quad-Tree method and then Cropped Quad-Tree method. It is observed that GPU computing is obviously faster than CPU computing and Cropped Quad-Tree method produces rapid results compared to Quad-Tree method as a result of performance. GPU computing method boosted approximately performance by up to 20 times compared to only CPU usage; furthermore, cropped quad-tree method boosted approximately performance of algorithm by up to 25 times on average dependent on screen and object size

The Use of Artificial Neural Networks in Simulation of Mobile Ground Vehicles

In this study, we have developed a platform which incorporates Artificial Neural Networks (ANNs) in simulating body dynamics of mobile ground vehicles (e.g. cars). This is a part of our research project in which we plan to provide a platform for educating the driver candidates in virtual environments: where the drivers can be educated fully in “Artificial Cities”. To start with, 6 different makes of cars with different engine properties has been simulated with the appropriate data provided by the manufacturers and rules of physics. A joystick steering wheel has been used to produce the necessary inputs for the ANN based physics engine. To train the network, Scaled Conjugate Gradient (SCG) and Levenberg-Marquardt (LM) learning algorithms and a logistic sigmoid transfer function have been used. The statistical error levels are negligible. The Absolute Fraction of Variance (R2) values for both the training and test data are about 99.999% and the mean error value for both data group is lesser than 0.5%

2021 Guideline for the Management of COPD Exacerbations: Emergency Medicine Association of Turkey (EMAT) / Turkish Thoracic Society (TTS) Clinical Practice Guideline Task Force

Chronic obstructive pulmonary disease (COPD) is an important public health problem that manifests with exacerbations and causes serious mortality and morbidity in both developed and developing countries. COPD exacerbations usually present to emergency departments, where these patients are diagnosed and treated. Therefore, the Emergency Medicine Association of Turkey and the Turkish Thoracic Society jointly wanted to implement a guideline that evaluates the management of COPD exacerbations according to the current literature and provides evidence-based recommendations. In the management of COPD exacerbations, we aim to support the decision-making process of clinicians dealing with these patients in the emergency setting