53 research outputs found

    Stacked Cross Validation with Deep Features: A Hybrid Method for Skin Cancer Detection

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    Detection of malignant skin lesions is important for early and accurate diagnosis of skin cancer. In this work, a hybrid method for malignant lesion detection from dermoscopy images is proposed. The method combines the feature extraction process of convolutional neural networks (CNN) with an ensemble learner called stacked cross-validation (CV). The features extracted by three different CNN architectures, namely, ResNet50, Xception, and VGG16 are used for training of four different baseline classifiers, which are support vector machines, k-nearest neighbors, artificial neural networks, and random forests. The stacked outputs of these classifiers are used to train a logistic regression model as a meta-classifier. The performance of the proposed method is compared with the baseline classifiers trained individually as well as AdaBoost classifier, another ensemble learner. Feature extraction with Xception architecture, outperforms all other benchmark models by achieving scores of 0.909, 0.896, 0.886, and 0.917 for accuracy, F1-score, sensitivity, and AUC, respectively

    Detection and classification of brain tumours from MRI images using faster R-CNN

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    Magnetic resonance imaging (MRI) is a useful method for diagnosis of tumours in human brain. In this work, MRI images have been analysed to detect the regions containing tumour and classify these regions into three different tumour categories: meningioma, glioma, and pituitary. Deep learning is a relatively recent and powerful method for image classification tasks. Therefore, faster Region-based Convolutional Neural Networks (faster R-CNN), a deep learning method, has been utilized and implemented via TensorFlow library in this study. A publicly available dataset containing 3,064 MRI brain images (708 meningioma, 1426 glioma, 930 pituitary) of 233 patients has been used for training and testing of the classifier. It has been shown that faster R-CNN method can yield an accuracy of 91.66% which is higher than the related work using the same dataset

    Moving vehicle detection and tracking at roundabouts using deep learning with trajectory union

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    The number of vehicles and turning movements at roundabouts provide important information for planning, design and operational analysis of roundabouts. The visual data collected through video cameras make it possible to determine such information via computer-based methods. In this work, a method for detecting, counting, and tracking vehicles in roundabout videos is proposed. There are two main contributions of the method, (i) only the moving vehicles are considered for tracking (moving vehicle detection) and (ii) the vehicle tracks output by the object tracking algorithms are processed to reduce the false track rate (trajectory union). The vehicle detection is performed using YOLOv4, and vehicle tracking throughout the video is accomplished by either Kalman filter or DeepSORT algorithm. The output of the proposed method is compared with both manual counting results and benchmark tracking results where the entry/exit matrix is generated using only YOLOv4 and an object tracker. In a 20-min video with 297 vehicles, absolute error reached by the proposed method is 14 vehicles which corresponds to normalized absolute error percentage of 1.571%. In the same video, the same error metrics obtained by the benchmark method are 33 vehicles and 3.704%. This error tends to increase together with the rate of vehicles in the video or number of legs in the roundabout. However, the rate of increment in the error is much lower than the rate of increment in the number of vehicles in the video. In addition, lower error rates are obtained when DeepSORT is used as the vehicle tracker

    Effects of Image Preprocessing on the Performance of Convolutional Neural Networks for Pneumonia Detection

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    Hastaya özel kemik modellemesi ile bir halka fiksatör sisteminin simülasyonu.

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    TEZ10969Tez (Doktora) -- Çukurova Üniversitesi, Adana, 2016.Kaynakça (s. 113-118) var.xii, 119 s. : res. (bzs. rnk.), tablo ; 29 cm.Harici fiksatörler ortopedi alanında kırık tespiti, boy uzatma ve deformite düzeltme amaçları için yaygın olarak kullanılmaktadır. Bu fiksatörlerde tipik olarak klinisyen, fiksatör halkalarını birbirine bağlayan çubukların boylarını değiştirerek kemik parçalarını anatomik olarak istenen pozisyona getirir. Bu işlem klinisyen tarafından, onun tecrübe ve uzmanlığına dayanılarak gerçekleştirilir. Alternatif olarak, fiksatörün matematiksel hesaplarını yapan bir yazılım yardımıyla aynı işlemlerin otomatize edildiği ticari sistemler bulunmaktadır. Klinisyenin tedavinin sonucunu önceden görmesine olanak sağlayacak uygun görselleştirme araçları günümüzde kullanılan yazılım sistemlerinde mevcut değildir. Bu tezde kanonik model ismindeki sağlıklı kemik modelini hastaya özel patolojik (örneğin deforme) kemik modeline dönüştüren yöntemler geliştirdik. Hastaya özel kemik modellerini otomatik olarak oluşturan, görselleştirme ve simülasyon araçlarına sahip bir kullanıcı arayüzü programlanmıştır. Kemik modelleme algoritması 19 farklı kemik modeli üzerinde değerlendirilmiştir ve algoritmanın efektif olduğu sonucuna varılmıştır. Buna ek olarak, görselleştirme aracı dört ortopedik uygulamanın simülasyonu şeklinde test edilmiştir. Örneklerin tamamında görselleştirme aracı kemik-fiksatör sistemini ve tedavi sürecini gerçekçi olarak göstermiştir. Klinisyenin uygulanan tedaviyi görselleştirmesi ve farklı tedavi senaryolarını önceden değerlendirmesine olanak saplayan görselleştirme aracı işle donatılmış bir arayüzün klinik olarak faydalı bir araç olacağına inanmaktayız.External ring fixators are widely used in orthopaedics for the purposes of fracture fixation, bone lengthening and deformity correction. In these fixators, the clinician typically brings the bone fragments to an anatomically desired position by changing the lengths of the rods connecting the fixator rings. This task is accomplished by the clinician based on experience and expertise. As an alternative, commercial systems exist where the same task is automatized with the help of an accompanying software that implements a mathematical model of the fixator. Proper visualization tools, that will allow the clinician to foresee the outcome of the treatment, are not present in the currently available software systems. In this thesis, we have developed methods to convert the model of a healthy bone, called the canonical model, to the patient-specific pathological (such as deformed) bone model. A graphical user interface (GUI) has been programmed with an advanced visualization and simulation tool that can automatically create the patient-specific bone model based on this approach. The bone modeling algorithm has been evaluated on a total of 19 different tibia and femur models and found to be effective. Furthermore, the visualization tool has been tested in the simulation of four orthopaedic procedures. In all examples, the visualization tool has provided a realistic depiction of the bone-fixator system and the treatment procedure. We believe that the developed GUI equipped with the visualization module could be a useful clinical tool where the clinician can visualize the applied treatment or evaluate different treatment scenarios a priori per patient

    Short-term traffic state estimation using breakpoint flow calculation and machine learning methods

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    Estimation of the state of road traffic conditions is gaining increasing attention in recent intelligent transportation systems. Accurate and real-time estimation of traffic condition changes is critical in the management and control of road network systems. Thus, efforts are been made to predict short-term traffic conditions based on measured traffic data such as speed, flow and density. In this work, the state of the traffic is estimated through a three-step process. First, both speed and flow predictions for 15-minute ahead are made for a particular freeway segment. Four different regression models are used for the prediction task, namely, multi-layer perceptron neural networks (MLPNN), support vector regression (SVR), gradient boosted decision trees (GBDT), and k-nearest neighbors (kNN). Next, the breakpoint (BP) flow is calculated using the distribution of these predicted speed and flow values. In the final step, these predictions are classified as belonging to a “stable state” or “metastable state” by using the calculated BP as the threshold between these states. According to the experimental results, the values for MLPNN are the highest for speed (0.8564) and flow (0.9862) predictions. An identical BP, 1050 pc/15min, is calculated for actual data as well as all prediction methods

    Tipburn disorder detection in strawberry leaves using convolutional neural networks and particle swarm optimization

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    Tipburn is a disorder that is caused by calcium deficiency in plants and may lead to decrements in crop yield. Therefore, it is important to detect tipburn for an appropriate treatment process. In this work, a sequential convolutional neural network (CNN) architecture was developed for tipburn detection in images of strawberry leaves. The parameters of the CNN architecture as well as the dropout and learning rates were determined through a two-stage search operation based on particle swarm optimization (PSO) algorithm. The resulting model, PSO-CNN, contains five convolutional layers and three fully-connected layers with varying number of filters and hidden units. The model development was performed using an original dataset of strawberry leaf images taken from the field under realistic conditions and has been made publicly available with this study. The performance of the PSO-CNN was compared with the performance of eight different benchmark CNN models, namely, VGG16, VGG19, MobileNetV2, EfficientNet, ResNetV2, NasNetMobile, InceptionV3 and InceptionResNetV2. According to the results obtained by ten independent re-runs of the classification task, PSO-CNN achieved the best average performance by 0.9895, 0.9863, and 0.9936 for accuracy, sensitivity, and specificity values, respectively. In addition, the number of parameters of the PSO-CNN model is smaller than those in the benchmark models. This means that PSO-CNN model requires relatively less amount of computation for an efficient model training and performing prediction on the test images. Finally, further experiments were performed on a multi-class problem to demonstrate the effectiveness of the PSO-CNN for tipburn detection

    Wireless communication protocols in smart agriculture: A review on applications, challenges and future trends

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    IoT based smart agriculture systems are important for efficient usage of lands, water, and energy resources. Wireless communication protocols constitute a critical part of smart agriculture systems because the fields, in general, cover a large area requiring system components to be placed at distant locations. There are various communication protocols with different features that can be utilized in smart agriculture applications. When designing a smart agriculture system, it is required to carefully consider the features of possible protocols to make a suitable and optimal selection. Therefore, this review paper aims to underline the specifications of the wireless communication protocols that are widely used in smart agriculture applications. Furthermore, application-specific requirements, which may be useful during the design stage of the smart agriculture systems, are highlighted. To accomplish these aims, this paper compares the technical properties and investigates the practical applications of five different wireless communication protocols that are commonly used in IoT applications: ZigBee, Wi-Fi, Sigfox, NB-IoT, and LoRaWAN. In particular, the inconsistencies in the technical properties of these protocols reported in different resources have been highlighted and the reason for this situation has been discussed. Considering the features offered by the protocols and the requirements of smart agriculture applications, the appropriateness of a particular protocol to a particular smart agriculture application is examined. In addition, issues about cost, communication quality, and hardware of the five protocols have been mentioned. The trending technologies with high potential for the future applications of smart agriculture have been introduced. In this context, the relation of the technologies like aerial systems, cellular communication, and big data analytics with wireless have been specified. Finally, the leading protocol and the smart agriculture application area have been highlighted through observing the year-based distribution of the recent publications. It has been shown that usage of LoRaWAN protocol has become more widespread in recent years.</p
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