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Publication Metadata only Anterior Segment Eye Abnormality Detection(ASSOC COMPUTING MACHINERY, 2023) Corbaci, Tolga; Bahcesehir UniversityVision is the most critical sense helping us to understand the world around us. Ophthalmology is an area of medicine that deals with the eye and vision. In many remote areas, people do not have access to ophthalmologists, and many go blind for preventable reasons. Awareness about eye health and early diagnosis is essential in eye health to prevent blindness. An artificial intelligence (AI) algorithm that can quickly detect eye disease is valuable and necessary. Anterior segment eye images are essential and easily obtained without additional equipment. In this study, I aimed to build an artificial intelligence algorithm to detect eye diseases from mobile photographs. I extracted and combined anterior segment eye photos from various publicly available datasets and labeled 3938 images as Normal (healthy) and 1094 images as Abnormal (unhealthy). I increased the data diversity by augmenting it with random flips and rotations: and then prepared it for AI training. I re-trained the algorithms trained in ImageNet Visual Recognition Challenge with the transfer learning method. I compared custom and pre-trained models. After evaluating the performance of the models with the test set, 98% accuracy and 97% F1 score were obtained with the Inception-ResNetV2 model.Publication Open Access Crop yield prediction using machine learning: A systematic literature review(Elsevier B.V., 2020) van Klompenburg, Thomas; Kassahun, Ayalew; Catal, Cagatay; van Klompenburg, Thomas, Information Technology Group, Wageningen University & Research, Wageningen, Netherlands; Kassahun, Ayalew, Information Technology Group, Wageningen University & Research, Wageningen, Netherlands; Catal, Cagatay, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, TurkeyMachine learning is an important decision support tool for crop yield prediction, including supporting decisions on what crops to grow and what to do during the growing season of the crops. Several machine learning algorithms have been applied to support crop yield prediction research. In this study, we performed a Systematic Literature Review (SLR) to extract and synthesize the algorithms and features that have been used in crop yield prediction studies. Based on our search criteria, we retrieved 567 relevant studies from six electronic databases, of which we have selected 50 studies for further analysis using inclusion and exclusion criteria. We investigated these selected studies carefully, analyzed the methods and features used, and provided suggestions for further research. According to our analysis, the most used features are temperature, rainfall, and soil type, and the most applied algorithm is Artificial Neural Networks in these models. After this observation based on the analysis of machine learning-based 50 papers, we performed an additional search in electronic databases to identify deep learning-based studies, reached 30 deep learning-based papers, and extracted the applied deep learning algorithms. According to this additional analysis, Convolutional Neural Networks (CNN) is the most widely used deep learning algorithm in these studies, and the other widely used deep learning algorithms are Long-Short Term Memory (LSTM) and Deep Neural Networks (DNN). © 2020 Elsevier B.V., All rights reserved.Publication Metadata only Effects of Network Depths on Semantic Image Segmentation by Weakly Supervised Learning, Zayif Denetimli Ogrenmeyle Semantik Imge Bolutlemede Ag Derinliginin Etkileri(Institute of Electrical and Electronics Engineers Inc., 2020) Bircanoglu, Cenk; Arica, Nafiz; Bircanoglu, Cenk, Adevinta, Paris, France; Arica, Nafiz, Bahçeşehir Üniversitesi, Istanbul, TurkeyWeakly Supervised Learning is one of the most interesting approaches that more complex labels are predicted by using related simple labels. In this study, we focus on segmentation problem by giving image class tags in learning stage. We examine how the number of layers and the usage of their output in Convolutional Neural Network affect the segmentation results. It is found that increasing the number of layers in the network has a positive effect on segmentation performance. After ResNet152 is determined as the most successful deep architecture in Pascal VOC2012 dataset, we construct a new architecture based on ResNet152. Experimental results show that proposed architecture outperforms the available studies tested on this particular dataset. In addition, we observe that early layers reach more general attributes for the object classes than the last layers and that these attributes can better identify the object boundaries. © 2021 Elsevier B.V., All rights reserved.Publication Metadata only Distracted driver detection by combining in-vehicle and image data using deep learning(Elsevier Ltd, 2020) Ömerustaoğlu, Furkan; Sakar, C. Okan; Kar, Gorkem; Ömerustaoğlu, Furkan, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Sakar, C. Okan, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Kar, Gorkem, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, TurkeyDistracted driving is among the most important reasons for traffic accidents today. Recently, there is an increasing interest in building driver assistance systems that detect the actions of the drivers and help them drive safer. In these studies, although some distinct data types such as the physical conditions of the driver, audio and visual features, car information are used, the main data source is the images of the driver that include the face, arms, and hands taken with a camera placed inside the car. In this work, we propose to integrate sensor data into the vision-based distracted driver detection model to improve the generalization ability of the system. With this purpose, we created a new data set that includes driver images and sensor data collected from real-world drives. Then, we constructed a two-stage distracted driving detection system to detect nine distracted behaviors. In the first stage, vision-based Convolutional Neural Network (CNN) models were created by transfer learning and fine-tuning methods. In the second stage, Long-Short Term Memory-Recurrent Neural Network (LSTM-RNN) models were created using sensor and image data together. We evaluate our system by two different fusion techniques and show that integrating sensor data to image-based driver detection significantly increases the overall performance with both of the fusion techniques. We also show that the accuracy of the vision-based model increases by fine-tuning the pre-trained CNN model using a related public dataset. © 2020 Elsevier B.V., All rights reserved.Publication Metadata only SkNet: A Convolutional Neural Networks Based Classification Approach for Skin Cancer Classes(Institute of Electrical and Electronics Engineers Inc., 2020) Jeny, Afsana Ahsan; Sakib, Abu Noman Md; Junayed, Masum Shah; Lima, Khadija Akter; Ahmed, Ikhtiar; Islam, Md Baharul; Jeny, Afsana Ahsan, Department of CSE, Daffodil International University, Dhaka, Bangladesh; Sakib, Abu Noman Md, Department of Cse, Khulna University of Engineering and Technology, Khulna, Bangladesh; Junayed, Masum Shah, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Lima, Khadija Akter, Department of CSE, Daffodil International University, Dhaka, Bangladesh; Ahmed, Ikhtiar, Department of CSE, Daffodil International University, Dhaka, Bangladesh; Islam, Md Baharul, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey, American University of Malta, Cospicua, MaltaSkin Cancer is one of the most common types of cancer. A solution for this globally recognized health problem is much required. Machine Learning techniques have brought revolutionary changes in the field of biomedical researches. Previously, It took a significant amount of time and much effort in detecting skin cancers. In recent years, many works have been done with Deep Learning which made the process a lot faster and much more accurate. In this paper, We have proposed a novel Convolutional Neural Networks (CNN) based approach that can classify four different types of Skin Cancer. We have developed our model SkNet consisting of 19 convolution layers. In previous works, the highest accuracy gained on 1000 images was 80.52%. Our proposed model exceeded that previous performance and achieved an accuracy of 95.26% on a dataset of 4800 images which is the highest acquired accuracy. © 2021 Elsevier B.V., All rights reserved.Publication Open Access Crowd Density Estimation by Using Attention Based Capsule Network and Multi-Column CNN(Institute of Electrical and Electronics Engineers Inc., 2021) Kizrak, Merve Ayyuce; Bolat, Bülent; Kizrak, Merve Ayyuce, Department of Artificial Intelligence Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Bolat, Bülent, Department of Electronics and Communication Engineering, Yıldız Teknik Üniversitesi, Istanbul, TurkeyWe propose a strategy that focuses on estimating the number of people in a crowd, one of the aims of crowd analysis, using static images or video images. While manual feature extraction was not performed with pixel and regression-based methods in the first studies on crowd analysis, recent studies use Convolutional Neural Networks (CNN) based models. However, it is still difficult to extract spatial information such as position, orientation, posture, and angular value for crowd estimation from a density map. This study uses capsule networks and routing by agreement algorithm as an attention module. Our proposed approach consists of both CNN and capsule network-based attention modules in a two-column deep neural network architecture. We evaluate our proposed approach compared with other state-of-the-art methods using three well-known datasets: UCF-QNRF, UCFCC50, UCSD, ShangaiTech Part A, and WorldExpo'10. © 2021 Elsevier B.V., All rights reserved.Publication Open Access CataractNet: An automated cataract detection system using deep learning for fundus images(Institute of Electrical and Electronics Engineers Inc., 2021) Junayed, Masum Shah; Islam, Md Baharul; Sadeghzadeh, Arezoo; Rahman, Saimunur; Junayed, Masum Shah, Daffodil International University, Dhaka, Bangladesh, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Islam, Md Baharul, Daffodil International University, Dhaka, Bangladesh, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey, College of Data Science and Engineering, American University of Malta, Cospicua, Malta; Sadeghzadeh, Arezoo, Daffodil International University, Dhaka, Bangladesh; Rahman, Saimunur, Commonwealth Scientific and Industrial Research Organisation, Canberra, AustraliaCataract is one of the most common eye disorders that causes vision distortion. Accurate and timely detection of cataracts is the best way to control the risk and avoid blindness. Recently, artificial intelligence-based cataract detection systems have been received research attention. In this paper, a novel deep neural network, namely CataractNet, is proposed for automatic cataract detection in fundus images. The loss and activation functions are tuned to train the network with small kernels, fewer training parameters, and layers. Thus, the computational cost and average running time of CataractNet are significantly reduced compared to other pre-trained Convolutional Neural Network (CNN) models. The proposed network is optimized with the Adam optimizer. A total of 1130 cataract and non-cataract fundus images are collected and augmented to 4746 images to train the model. For avoiding the over-fitting problem, the dataset is extended through augmentation before model training. Experimental results prove that the proposed method outperforms the state-of-the-art cataract detection approaches with an average accuracy of 99.13%. © 2021 Elsevier B.V., All rights reserved.Publication Metadata only Towards Stereoscopic Video Deblurring Using Deep Convolutional Networks(Springer Science and Business Media Deutschland GmbH, 2021) Imani, Hassan; Islam, Md Baharul; Bebis, G.; Athitsos, V.; Yan, T.; Lau, M.; Li, F.; Shi, C.; Yuan, X.; Mousas, C.; Bruder, G.; Imani, Hassan, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Islam, Md Baharul, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, TurkeyThese days stereoscopic cameras are commonly used in daily life, such as the new smartphones and emerging technologies. The quality of the stereo video can be affected by various factors (e.g., blur artifact due to camera/object motion). For solving this issue, several methods are proposed for monocular deblurring, and there are some limited proposed works for stereo content deblurring. This paper presents a novel stereoscopic video deblurring model considering the consecutive left and right video frames. To compensate for the motion in stereoscopic video, we feed consecutive frames from the previous and next frames to the 3D CNN networks, which can help for further deblurring. Also, our proposed model uses the stereoscopic other view information to help for deblurring. Specifically, to deblur the stereo frames, our model takes the left and right stereoscopic frames and some neighboring left and right frames as the inputs. Then, after compensation for the transformation between consecutive frames, a 3D Convolutional Neural Network (CNN) is applied to the left and right batches of frames to extract their features. This model consists of the modified 3D U-Net networks. To aggregate the left and right features, the Parallax Attention Module (PAM) is modified to fuse the left and right features and create the output deblurred frames. The experimental results on the recently proposed Stereo Blur dataset show that the proposed method can effectively deblur the blurry stereoscopic videos. © 2021 Elsevier B.V., All rights reserved.Publication Metadata only DeepPyNet: A Deep Feature Pyramid Network for Optical Flow Estimation(IEEE Computer Society, 2021) Jeny, Afsana Ahsan; Islam, Md Baharul; Aydin, Tarkan; Cree, M.J.; Jeny, Afsana Ahsan, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Islam, Md Baharul, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Aydin, Tarkan, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, TurkeyRecent advances in optical flow prediction have been made possible by using feature pyramids and iterative refining. Though downsampling in feature pyramids may cause foreground items to merge with the background, the iterative processing could be incorrect in optical flow experiments. Particularly the outcomes of the movement of narrow and tiny objects can be more invisible in the flow scene. We introduce a novel method called DeepPyNet for optical flow estimation that includes feature extractor, multi-channel cost volume, and flow decoder. In this method, we propose a deep recurrent feature pyramid-based network for the end-to-end optical flow estimation. The feature extraction from each pixel of the feature map keeps essential information without modifying the feature receptive field. Then, a multi-scale 4D correlation volume is built from the visual similarity of each pair of pixels. Finally, we utilize the multi-scale correlation volumes to continuously update the flow field through an iterative recurrent method. Experimental results demonstrate that DeepPyNet significantly eliminates flow errors and provides state-of-the-art performance in various datasets. Moreover, DeepPyNet is less complex and uses only 6.1M parameters 81% and 35% smaller than the popular FlowNet and PWC-Net+, respectively. © 2022 Elsevier B.V., All rights reserved.Publication Metadata only Deep Covariance Feature and CNN-based End-to-End Masked Face Recognition(Institute of Electrical and Electronics Engineers Inc., 2021) Junayed, Masum Shah; Sadeghzadeh, Arezoo; Islam, Md Baharul; Struc, V.; Ivanovska, M.; Junayed, Masum Shah, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Sadeghzadeh, Arezoo, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Islam, Md Baharul, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey, College of Data Science and Engineering, American University of Malta, Cospicua, MaltaWith the emergence of the global epidemic of COVID-19, face recognition systems have achieved much attention as contactless identity verification methods. However, covering a considerable part of the face by the mask poses severe challenges for conventional face recognition systems. This paper proposes an automated Masked Face Recognition (MFR) system based on the combination of a mask occlusion discarding technique and a deep-learning model. Initially, a pre-processing step is carried out in which the images pass three filters. Then, a Convolutional Neural Network (CNN) model is proposed to extract the features from unoccluded regions of the faces (i.e., eyes and forehead). These feature maps are employed to obtain covariance-based features. Two extra layers, i.e., Bitmap and Eigenvalue, are designed to reduce the dimension and concatenate these covariance feature matrices. The deep covariance features are quantized to codebooks combined based on Bag-of-Features (BoF) paradigm. Finally, a global histogram is created based on these codebooks and utilized for training an SVM classifier. The proposed method is trained and evaluated on Real-World-Masked-Face-Recognition-Dataset (RMFRD) and Simulated-Masked-Face-Recognition-Dataset (SMFRD) achieves an accuracy of 95.07% and 92.32 %, respectively, showing its competitive performance compared to the state-of-the-art. Experimental results prove that our system has high robustness against noisy data and illumination variations. © 2025 Elsevier B.V., All rights reserved.