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Öğe Artificial Neural Network Based on Rotation Forest for Biomedical Pattern Classification(IEEE, 2013) Koyuncu, Hasan; Ceylan, RahimeThe novel classifier system based on ensemble classifier is proposed in this paper. Rotation forest algorithm based on principal component algorithm was used as ensemble classifier method. In presented classifier system, artificial neural network was used as base classifier in this ensemble classifier system. Rotation forest structure has been generally realized with decision trees in literature. But, multilayer perceptron neural network was utilized as base classifier in rotation forest structure in our study. However, principal component analysis was used for obtaining different feature sets from original data set. The proposed RF-ANN structure was applied to Wisconsin breast cancer data taken form UCI Database. The obtained results were compared with the results of neural network optimized particle swarm optimization (PSO-ANN). The realized experimental studies were represented that RF-ANN structure was successful than PSO-ANN structure. RF-ANN classified breast cancer dataset with 98.05% classification accuracy using 9 classifiers.Öğe BT görüntülerinden sürrenal lezyonların belirlenmesi ve sınıflandırılması(Selçuk Üniversitesi Fen Bilimleri Enstitüsü, 2018-03-30) Koyuncu, Hasan; Ceylan, RahimeSürrenal lezyonlar böbreküstü bezlerinde meydana gelen ve genellikle rastlantısal olarak tespit edilen tümör tipleridir. Sürrenal lezyonların tespitinde ve karakterizasyonunda genellikle Bilgisayarlı Tomografi (BT) görüntüleme ve biyopsiden faydalanılır. Ancak sürrenal lezyonların anatomik konumu nedeniyle biyopsi işlemi oldukça zordur. Bu durum, bir Bilgisayar Destekli Teşhis (BDT) sisteminin gerekliliğini ortaya koymaktadır. Gerçekleştirilen tez çalışmasında, sürrenal lezyonların tespiti ve karakterizasyonu için bir BDT sistemi tasarlanmıştır. Tasarlanan BDT sistemi; abdomen segmentasyonu, gürültü ve yağ dokusu eliminasyonu, lezyon segmentasyonu ve lezyon sınıflandırma olmak üzere 4 aşamadan oluşmaktadır. Tez çalışması, Selçuk Üniversitesi Tıp Fakültesi Radyoloji Anabilim Dalı' ndan alınan 98 BT görüntüsü ile gerçekleştirilmiştir. Önerilen BDT sisteminin abdomen segmentasyonu aşamasında eğitim, test ve validasyon veri setlerinde sırasıyla %99.21, %99.54 ve %99.78 ortalama doğruluk değerleri elde edilmiştir. Gürültü eliminasyonu aşamasında literatürdeki farklı yöntemler karşılaştırılmış ve en iyi sonuç (%93.16 doğruluk) Blok Eşleştirme & 3B Filtreleme (BE3BF) algoritması ile bulunmuştur. Yağ dokusu ve gürültü eliminasyonu aşamasında literatüre göre 0.157 dB üstünlük sağlanmıştır. Sürrenal lezyonların segmentasyonu için önerilen sistem ile %83.06 Dice, %86.44 duyarlılık ve %99.66 özgüllük oranlarına ulaşılmıştır. Ayrıca önerilen BDT sisteminin son aşaması olan sürrenal lezyonların sınıflandırılmasında, dalgacık transformu ve optimizasyon temelli yapay sinir ağı ile %80.70 doğruluk ve %75 duyarlılık elde edilmiştir.Öğe Classification of Adrenal Lesions by Bounded PSO-NN(IEEE, 2017) Koyuncu, Hasan; Ceylan, RahimeAdrenal glands are the organs at which vitally important hormones are released. In adrenal glands, different kind of benign and malign lesions can arise. Herein, Dynamic Computed Tomography (dynamic CT) is the most used scan type for definition of lesion types. On the events that dynamic CT underwhelms, biopsy process is performed which is difficultly implemented because of the location of adrenal glands. During biopsy process, different complications can happen since adrenals glands are surrounded by spleen, lung, etc. At this point, a decision support system is needed for helping to medical experts. In this study, a Region of Interest (ROI) is defined that includes adrenal lesions. After that, feature extraction is realized by using Gray-Level Co-Occurance Matrix (GLCM) and the second-order statistics. At classification part, Neural Network (NN) and a novel approach including NN (Bounded PSO-NN) are evaluated by utilizing from three performance metrics. As a result, it's confirmed that Bounded PSO-NN classifies the malign and benign patterns more accurately which obtained by analysis taken from ROI.Öğe A Hybrid Tool on Denoising and Enhancement of Abdominal CT Images before Organ & Tumour segmentation(IEEE, 2017) Koyuncu, Hasan; Ceylan, RahimeMost of abdominal CT images include Gaussian noise, and CT scans form a blurry vision because of the internal fat tissue inside of abdomen. These two handicaps (noise and fat tissue) constitute an impediment in front of an accurate abdominal organ & tumour segmentation. Also segmentation techniques generally fall into error on segmentation of close grayscale regions. Therefore, denoising and enhancement parts are crucial for better segmentation results on CT images. In this paper, we form a tool including three efficient algorithms for the purpose of image enhancement before abdominal organ & tumour segmentation. At first, the denoising process is realized by Block Matching and 3D Filtering (BM3D) algorithm for elimination of Gaussian noise stated in arterial phase CT images. At second, Fast Linking Spiking Cortical Model (FL-SCM) is used for removing the internal fat tissue. At last, Otsu algorithm is processed to remove the redundant parts within the image. In experiments, Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) index are used to evaluate the performance of proposed method, and a visual comparison is presented. According to results, it is seen that proposed tool obtains the best PSNR and SSIM values in comparison with two steps of pipeline (FL-SCM and BM3D & FL-SCM). Consequently, BM3D & FL-SCM & Otsu (BFO) ensures a clean abdomen particularly for segmentation of liver, spleen, pancreas, adrenal tumours, aorta, ribs, spinal cord and kidneys.Öğe A New Breakpoint in Hybrid Particle Swarm-Neural Network Architecture: Individual Boundary Adjustment(WORLD SCIENTIFIC PUBL CO PTE LTD, 2016) Ceylan, Rahime; Koyuncu, HasanNeural Network (NN) is an effective classifier, but it generally uses the Backpropagation type algorithms which are insufficient because of trapping to local minimum of error rate. For elimination of this handicap, stochastic optimization algorithms are used to update the parameters of NN. Particle Swarm Optimization (PSO) is one of these providing a robust coherence with NN. In realized studies about Hybrid PSO-NN, position and velocity boundaries of weight and bias are chosen equal or set free in space which leave the performance of PSO-NN in suspense. In this paper, the limitations of weight velocity (wv), weight position (wp), bias velocity (bv) and bias position (bp) are diversely changed and their effects on the output of hybrid structure are examined. Concerning this, the formed structure is called as Bounded PSO-NN on account of adjusting the optimum operating conditions (intervals). On performance evaluation, proposed method is tested on binary and multiclass pattern classiffication by using six medical datasets: Wisconsin Breast Cancer (WBC), Pima Indian Diabetes (PID), Bupa Liver Disorders (BLD), Heart Statlog (HS), Breast Tissue (BT) and Dermatology Data (DD). Upon analyzing the results, it was revealed that Bounded PSO-NN has a faster processing time than general PSO-NNs in which set-free and wpi=bpi and wvi=bvi conditions are settled. The superiority in terms of processing time is about 199 s (set-free) and 307 s (wpi=bpi and wvi=bvi) for training, about 16 ms (set-free) and 9ms (wpi=bpi and wvi=bvi) for test. In terms of classification performance, PSO-NN (set-free condition), PSO-NN (wpi=bpi & wvi=bvi) and PSO-NN with individual boundary adjustment (bounded PSO-NN) respectively achieves to accuracy rates as 69.84%, 95.31% and 97.22% on WBC, 47.01%, 76.69% and 77.73% on PID, 55.36%, 67.54% and 73.91% on BLD, 64.82%, 81.48% and 85.56% on HS, 75%, 92.31% and 100% on BT, 27.47%, 92.31% and 100% on DD. In the light of experiments, it is seen that Bounded PSO-NN is better than general PSO-NNs for obtaining the optimum results. Consequently, the importance of limitations is clarified and it is proven that each limitation must be adjusted individually, not be set free or not be chosen equal.Öğe A Novel Rotation Forest Modality Based on Hybrid NNs: RF (ScPSO-NN)(ELSEVIER SCIENCE BV, 2019) Ceylan, Rahime; Koyuncu, HasanNeural Network (NN), hybrid NN methods and Rotation Forest (RF) ensemble classifier are preferred in pattern analysis owing to their ability for finding efficient solutions on different problems. NN architecture usually includes backpropagation type algorithms in which error is exposed to fluctuations. Hybrid NN methods are generally designed to improve the classification performance of NN. Scout Particle Swarm Optimization (ScPSO) is one of these optimization algorithms including the effective parts of Particle Swarm Optimization (PSO) and Artificial Bee Colony Optimization (ABC). Moreover, RF algorithm usually indicates the same performance as in hybrid NN methods, although it is comprised of Decision Tree (DT) classifiers. At this point, our paper investigates whether RF using the hybrid NNs can outperform other ensemble classifiers in binary-medical pattern classification, or not. With this intention, PSO, ABC and ScPSO are placed in NN algorithms instead of back propagation, and hybrid methods (PSO-NN, ABC-NN and ScPSO-NN) are realized. As a result, RF (PSO-NN), RF (ABC-NN) and RF (ScPSO-NN) architectures are obtained. Classification Accuracy (CA), Area Under Curve (AUC), Sensitivity, Specificity, F-measure, Gmean and Precision metrics are used for a statistical performance comparison, and a test based on 2-fold cross validation method was realized on five medical datasets. (C) 2017 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University.Öğe Optic Disc Segmentation with Kapur-ScPSO Based Cascade Multithresholding(SPRINGER INTERNATIONAL PUBLISHING AG, 2016) Koyuncu, Hasan; Ceylan, RahimeThe detection of significant retinal regions (segmentation) constitutes an indispensible need for computer aided diagnosis of retinal based diseases. At this point, image segmentation algorithm is wanted to be quick in order to spare time for feature selection and classification parts. In this paper, we deal with the fast and accurate segmentation process of optic discs in retinal images. For this purpose, a cascade multithresholding (CMT) process is proposed by a novel optimization algorithm (Scout Particle Swarm Optimization) and an efficient cost function (Kapur). Scout Particle Swarm Optimization (ScPSO) is originated from Particle Swarm Optimization (PSO) and improves standard PSO by using a necessary part taken from Artificial Bee Colony (ABC) Optimization. In other words, the most important handicap of PSO (regeneration of useless particles) is eliminated via the formation of ScPSO that can be obtained by adding the scout bee phase from ABC into standard PSO. In this study, this novel method (ScPSO) constitutes the optimization part of multithresholding process. Kapur function is preferred as being the cost function to be used in ScPSO, since Kapur provides low standard deviations on output of optimization based multithresholding techniques in literature. In this manner, a well-combined structure (Kapur-ScPSO) is generated for cascade multithresholding. Optic disc images taken from DRIVE database are used for statistical and visual comparison. As a result, Kapur-ScPSO based CMT can define the optic disc quickly (7-8 s) with the rates of 77.08 % precision, 57.89 % overlap and 95.59 % accuracy.Öğe Rotasyonel orman ile biyomedikal örüntü sınıflandırma(Selçuk Üniversitesi Fen Bilimleri Enstitüsü, 2013-12-09) Koyuncu, Hasan; Ceylan, RahimeÖrüntü sınıflandırma, biyomedikalde, tanı ve teşhis aşamalarında bilim insanlarına yardımcı olmaktadır. Literatürde bu amaçla birçok sınıflandırıcı sistem tasarımı gerçekleştirilmiştir. Bu metotlardan biri de yapay sinir ağları (YSA)' dır. Ayrıca, çeşitli optimizasyon teknikleri, YSA yapısındaki ağırlık-bias değerlerinin ayarlanması ve performansın artırılması için güncelleme bölümüne entegre edilmektedir. Tez çalışmasının ilk aşamasında, YSA' daki ağırlık-bias değerlerinin güncellenmesi için, YSA güncelleme bölümü Parçacık Sürü Optimizasyonu (PSO) temelli oluşturulmuştur. Bu sayede sınıflandırma doğruluğu artırılmıştır. İkinci aşamada, içerisinde birden fazla hibrit PSO-YSA sınıflandırıcı birimi bulunduran Rotasyonel Orman (RO (hibrit PSO-YSA)) yapısının tasarımı gerçekleştirilmiştir. Böylece tek bir temel sınıflandırıcı (YSA) veya bu temel sınıflandırıcıdan daha iyi performansa sahip olan hibrit bir temel sınıflandırıcı (hibrit PSO-YSA) kullanmak yerine, RO (hibrit PSO-YSA) sınıflandırıcı topluluğunu kullanmanın daha uygun olacağı tespit edilmiştir. Bunun yanısıra hibrit yapı içerisindeki ağırlık ve bias değelerinin konum-hız sınırlamalarında, limit aralıklarının eşit alınmaması veya serbest bırakılmaması gerektiği, her bir durum için (ağırlık konum, ağırlık hız, bias konum ve bias hız limitleri) optimum sınırların tespit edilmesi gerektiği görülmüştür. Böylece, literatürdeki hibrit PSO-YSA yapılarından farklı optimize edilmiş bir ağ yapısı sunulmuştur. Tez çalışmasında sunulan yapılar (hibrit PSO-YSA ve RO (hibrit PSO-YSA)) göğüs kanseri verileri üzerinde test edilmiştir. Sonuçlar literatürde elde edilen sonuçlarla karşılaştırılarak değerlendirneler sunulmuştur.Öğe Scout Particle Swarm Optimization(SPRINGER-VERLAG BERLIN, 2015) Koyuncu, Hasan; Ceylan, RahimeParticle Swarm Optimization is a robust optimization algorithm proved itself in various technical areas like training of classifiers, image classification and function optimization, etc. It simulates the foraging behaviour of bird swarms. While doing that, it uses velocity and position metrics for directing its particles to food. Concerning this, it has various advantages like high convergence, speedy process capability and a few parameters to be adjusted. But it has a significant disadvantage restricting the performance. This handicap is regeneration of the particle which couldn't improve itself along iterations. Moreover, Artificial Bee Colony Optimization (ABC) is a valuable optimization algorithm imitating the foraging behaviour like PSO. However, ABC uses honey bees grouped as employed bees, onlooker bees and scout bees. The employed bee and onlooker bee phases do the same work with velocity and position concepts in PSO. But, scout bee phase regenerates the useless particles in order to achieve higher performance by upgrading diversity. Therefore, it's seen that addition of scout bee phase into PSO looks like a smart idea. So, in this study, Scout PSO (ScPSO) algorithm is designed which is more effective and useful than PSO. For performance analysis of ScPSO, it was used in training of NN classifier. Furthermore, ScPSO-NN is compared with NN and PSO-NN methods on medical pattern classification. For this purpose, Wisconsin Breast Cancer-Original (WBC), Pima Indian Diabetes (PID), Heart Statlog (HS) and Bupa Liver Disorders (BLD) datasets are used and test process is realized by 10-fold cross validation method. As a result, ScPSO-NN achieves classification accuracies as 97.51% (WBC), 78.13% (PID), 86.30% (HS) and 75.07% (BLD).
