A Novel Hybrid Image Segmentation Method for Detection of Suspicious Regions in Mammograms Based on Adaptive Multi-Thresholding (HCOW)

Suspicious region segmentation is one of the most important parts of CAD systems that are used for breast cancer detection in mammograms. In a CAD system, there can be so many suspicious regions determined for a mammogram because of the complex structure of the breast. This study proposes a hybrid thresholding method to use in the CAD systems for efficient segmentation of the mammograms and reducing the number of the suspicious regions. The proposed method provides fully-automatic segmentation of the suspicious regions. This method is based on determining an adaptive multi-threshold value by using three different techniques together. These techniques are Otsu multilevel thresholding, Havrda & Charvat entropy, and w-BSAFCM algorithm that was developed by the authors of this paper for image clustering applications. In the proposed method, segmentation of a mammogram is performed on two sub-images obtained from that mammogram, the pectoral muscle and the breast region to prevent any information loss. The method was tested on 55 mass-mammograms and 210 non-mass mammograms of the mini-MIAS database, and it was compared with Shannon, Renyi, and Kapur entropy methods and with some of the related studies from the literature. The segmentation results of the tests were evaluated in terms of the number of suspicious regions, the number of correctly detected masses, and the performance measure parameters, accuracy, false-positive rate, specificity, volumetric overlap, and dice similarity coefficient. According to the evaluations, it was shown that the proposed method can both successfully locate the mass regions and significantly reduce the number of the non-mass suspicious regions on the mammograms.WOS:0006732710000012-s2.0-8511761585

GENDER ESTIMATION WITH CONVOLUTIONAL NEURAL NETWORKS USING FINGERTIP IMAGES

Bringing several innovations to our daily life, the importance of artificial intelligence technology hasbeen increasing day by day and has created new fields for researchers. Gender classification is also animportant research topic in the field of artificial intelligence. Studies on gender prediction from face,body, and even fingerprint images have been done. Also, today, biometric recognition systems havereached levels that can determine people's fingerprints, face, iris, palm prints, signature, DNA, andretina. In this study, various models were trained and tested on gender classification from fingertipimages. In the, a ready dataset was not used and finger images were collected from more than 200people. Rotation, cutting, and background reduction are applied to the collected images and madeready for the training. 4 different network models were set in the fieldwork. Data augmentation andtransfer learning were used in these models. Working in a limited area, the model we created hasachieved high-performance results, for all that the quality and angles of each image are different. Themodel proposed in this study has a performance rate of 86.39%

GENDER ESTIMATION WITH CONVOLUTIONAL NEURAL NETWORKS USING FINGERTIP IMAGES

Bringing several innovations to our daily life, the importance of artificial intelligence technology hasbeen increasing day by day and has created new fields for researchers. Gender classification is also animportant research topic in the field of artificial intelligence. Studies on gender prediction from face,body, and even fingerprint images have been done. Also, today, biometric recognition systems havereached levels that can determine people's fingerprints, face, iris, palm prints, signature, DNA, andretina. In this study, various models were trained and tested on gender classification from fingertipimages. In the, a ready dataset was not used and finger images were collected from more than 200people. Rotation, cutting, and background reduction are applied to the collected images and madeready for the training. 4 different network models were set in the fieldwork. Data augmentation andtransfer learning were used in these models. Working in a limited area, the model we created hasachieved high-performance results, for all that the quality and angles of each image are different. Themodel proposed in this study has a performance rate of 86.39%

Generative Networks and Royalty-Free Products

In recent years, with the increasing power of computers and Graphics Processing Units (GPUs), vast variety ofdeep neural networks architectures have been created and realized. One of the most interesting and generativetype of the networks are Generative Adversarial Networks (GANs). GANs are used to create things such asmusic, images or a film scenerio. GANs consist of two networks working simultaneously. Generative networkcaptures data distribution and discriminative network estimates the probability of the Generative Networkoutput, coming from training data of discriminative network. The objective is to both maximizing the generativenetwork products reality and minimize the discriminative network classification error. This procedure is aminimax two-player game. In this paper, it has been aimed to review the latest studies with GANs, to gather therecent studies in an article and to discuss the possible issues with royalty free products created by GANs. Withthis aim, from 2018 to today, the studies on GANs have been gathered to the citation numbers. As a result, therecent studies with GANs have been summarized and the potential issues related to GANs have been submitted.Son yıllarda, bilgisayarların ve Grafik İşlem Birimlerinin (GPU'ların) artan gücüyle, çok çeşitli derin sinir ağları mimarileri oluşturulmuş ve gerçekleştirilmiştir. En ilginç ve üretken ağ türlerinden biri de Üretken Çekişmeli Ağlardır (GANs). GAN ağları müzik, görüntü ve film senaryolarının üretiminde kullanılmaktadır. GAN ağları eş zamanlı çalışan iki ağ yapısından oluşmaktadır. Üretici ağ, veri dağıtımını üstlenmekte ayırıcı ağlar ise, ayrımcı ağın veya üretken ağ ürününün eğitim verilerinden gelen Üretken Ağ çıktısının olasılığını tahmin etmektedir. Amaç hem üretken ağın ürettiği verinin gerçekliğini maksimize ederken, ayırıcı ağın da hatasını minimize etmektir. Bu süreç iki oyunculu bir minimax problemidir. Bu çalışmada GAN ağları ile ilgili yapılan son çalışmaların gözden geçirilerek bir makale altında bir araya getirilmesi ve GAN ağları ile üretilen telifsiz ürünler ile ortaya çıkacak olası konuların tartışılması amaçlanmıştır. Bu amaç ile 2018’den günümüze bu konuda yapılmış olan çalışmaların atıf sayısı en yüksek olanları bu çalışmada bir araya getirilmiştir. Sonuç olarak GAN ağları ile yapılmış bu çalışmaların özet ve sonuçları bir tablo haline getirilerek sunulmaktadır. Bu şekilde GAN ağlarının mevcut uygulamaları bu çalışmada ortaya konulmaktadır. Yine GAN ağları ile ilgili olası sorunların ne olacağı da bu çalışmada sunulmaktadır

A novel handwritten Turkish letter recognition model based on convolutional neural network

Convolutional neural networks have provided state-of-the-art solutions for many subfields of computer vision. While there exist many studies in the literature for several languages, studies for handwritten Turkish character recognition lack in the research field. To this end, we propose a novel handwritten Turkish letter recognition model based on a convolutional neural network. Since, to the best of our knowledge, there do not exist any publicly available handwritten Turkish letters datasets, we constructed a handwritten Turkish letters dataset that consists of 25,875 samples. To compare the performance of the proposed model with the related work, three state-of-the-art models, namely, VGG19, InceptionV3, and Xception, were utilized through the transfer learning technique. When these models were evaluated on the handwritten Turkish letter dataset, the proposed model's accuracy was calculated as high as 96.07% which was higher than the benchmark models. To measure the generalization ability of the proposed model, it was evaluated on a gold standard dataset, namely, EMNIST, and has achieved an accuracy of 80.54% which was higher than the benchmark models. Finally, the proposed model was trained and evaluated on the EMNIST dataset and it has achieved an accuracy of 94.61% which outperformed the related work.WOS:0006557492000012-s2.0-8510656950

Hibrit Çok-Amaçlı Rüzgar Güdümlü Optimizasyon Algoritması

Temel anlamda optimizasyon, bir veya birden fazla problemin belirli koşullar altındaki en iyi çözümlerini bulmaişlemidir. Günümüzde bu problemlerin çözümü için klasik yöntemler ve sezgisel yöntemler kullanılmaktadır.Sezgisel yöntemlerden biri olan Rüzgar Güdümlü Optimizasyon algoritması, rüzgarın atmosfer içerisindekihareketini temel alarak atmosferik dinamik eşitlikten yararlanan tek amaçlı optimizasyon problemlerine çözümarayan bir algoritmadır.Bu çalışmada çok-amaçlı optimizasyon problemlerinin çözümü için Rüzgar Güdümlü Optimizasyon algoritmasıyeniden düzenlenmiştir. Çok-amaçlı optimizasyon problemlerinde elde edilen sonuçların gerçek sonuçlara nekadar yakınsadığı ve bu sonuçların ne kadar çeşitli olduğu kullanılan yöntemlerin performansı hakkında bilgivermektedir. Baskın olmayan sıralama, ağırlıklı toplam, normal sınır kesişimi gibi metotlar çok-amaçlıoptimizasyon problemlerinde sıklıkla kullanılan yaklaşımlardır. Bu yaklaşımlardan bazıları çeşitlilik açısından önplana çıkarken bazılarının ise en iyi sonuca daha iyi yakınsadığı gözlenmiştir. Bu çalışmanın temel amacı eldeedilen çözümleri hem çeşitlilik hem de yakınsama açısından en iyi hale getirmektir.Bu amaç kapsamında baskın olmayan sıralama ve adaptif ızgara yaklaşımları bir arada kullanılarak yeni bir hibrityaklaşım geliştirilmiştir. Daha iyi bir yakınsama için baskın olmayan sıralama, çeşitlilik için adaptif ızgarayaklaşımı bir arada kullanılmıştır. Geliştirilen bu hibrit yaklaşım test problemleri ve doğrusal olmayan denklemsistemlerinde test edilerek sonuçları literatürde iyi bilinen Baskın Olmayan Sıralamalı Genetik Algoritma (NSGAII) ve Çok-Amaçlı Parçacık Sürü Optimizasyonu (MOPSO) algoritmaları ile karşılaştırılmıştır. Deneysel sonuçlarincelendiğinde çeşitlilik ve yakınsama performansı açısından geliştirilen hibrit yaklaşımın kabul edilebilir olduğugözlenmiştir.Basically, optimization is the process of finding the best solutions for one or more problems under certain conditions. Today, classical methods and heuristic methods are used to solve these problems. Wind Driven Optimization algorithm, which is one of the heuristic methods, is an algorithm that seeks solutions for singleobjective optimization problems that benefit from atmospheric dynamic equality based on the movement of the wind in the atmosphere. In this study, Wind Driven Optimization algorithm was rearranged to solve multi-objective optimization problems. In multi-objective optimization problems, the performance of the used method depends on how closely the results obtained converge to the actual results and how diverse these results are. Methods such as non-dominant sorting, weighted sum, normal boundary intersection are frequently used approaches in multi-objective optimization problems. While some of these approaches have more diverse to results, others have been observed to better converge. The main purpose of this study is to optimize the solutions obtained in terms of both diversity and convergence. Within this scope, a new hybrid approach has been developed by using non-dominant sorting and adaptive grid approaches. Non-dominant sorting used for better convergence and adaptive grid approach is used for diversity. The developed hybrid approach has been tested in test problems and nonlinear equation systems. Results have been compared with Non-Dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO). When the experimental results were examined, it was observed that the hybrid approach developed in terms of diversity and convergence performance was acceptable

Artificial Neural Network-Based 4-D Hyper-Chaotic System on Field Programmable

In this presented study, a 4-D hyper-chaotic system newly proposed to the literature, has been implemented as Multi-Layer Feed-Forward Artificial Neural Network-based on FPGA chip with 32-bit IEEE-754-1985 floating-point number standard to be utilized in real time chaos-based applications. In the first step of the study, 4-D hyper-chaotic system has been numerically modeled on FPGA using Dormand-Prince numeric algorithm. In the second step,the data set (4X10,000) obtained from Matlab-based numeric model has been divided into two parts as training data set (4X8,000) and test data set (4X2,000) to create ANN-based 4-D hyper-chaotic system. A Multi-Layer Feed-Forward ANN structure with 4 inputs and 4 outputs has been constructed for ANN-based 4-D hyper-chaotic system. This structure has only one hidden layer and there are 8 neurons having Tangent Sigmoid activation function used as the activationfunction in each neuron.2.58E-07 Mean Square Error (MSE) value has been obtained from the training of ANN-based 4-D hyper-chaotic system. In the third step, after the successful training of ANN-based 4-D hyper-chaotic system, the design of ANN-based 4-D hyper-chaotic system has been carried out on FPGA by taking the bias and weight values of the ANN structure as reference. In this step, at first, Matlab-based Feed-Forward Multi-Layer 4X8X4 network structure has been coded in Very High Speed Integrated Circuit Hardware Description Language (VHDL) to be implemented on FPGA chips. Then, the bias and weight values of the ANN structure has been converted from decimal number system to floating-point number standard and these converted values have been embedded into the network structure.In the last step, the ANN-based 4-D hyper-chaotic system designed on FPGA has been synthesized and tested using Xilinx ISE Design Suite. The chip statistics have been given after the Place&Route process carried out for the Virtex XC6VHX255T-3FF1155 FPGA chip. The maximum operating frequency of ANN-based 4-D hyper-chaotic system on FPGA has been obtained as 240.861 MHZ.2-s2.0-8509148055