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Publication Metadata only CataractNet: An Automated Cataract Detection System Using Deep Learning for Fundus Images(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021) Junayed, Masum Shah; Islam, Md Baharul; Sadeghzadeh, Arezoo; Rahman, Saimunur; Daffodil International University; Bahcesehir University; Commonwealth Scientific & Industrial Research Organisation (CSIRO); CSIRO Data61Cataract 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%.Publication Metadata only ScarNet: Development and Validation of a Novel Deep CNN Model for Acne Scar Classification With a New Dataset(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2022) Junayed, Masum Shah; Islam, Md Baharul; Jeny, Afsana Ahsan; Sadeghzadeh, Arezoo; Biswas, Topu; Shah, A. F. M. Shahen; Daffodil International University; Bahcesehir University; Multimedia University; Yildiz Technical UniversityAcne scarring occurs in 95% of people with acne vulgaris due to collagen loss or gains when the body is healing the damages of the skin caused by acne inflammation. Accurate classification of acne scars is a vital factor in providing a timely, effective treatment protocol. Dermatologists mainly recognize the type of acne scars manually based on visual inspections, which are time- and energy-consuming and subject to intra- and inter-reader variability. In this paper, a novel automated acne scar classification system is proposed based on a deep Convolutional Neural Network (CNN) model. First, a dataset of 250 images from five different classes is collected and labeled by four well-experienced dermatologists. The pre-processed input images are fed into our proposed model, namely ScarNet, for deep feature map extraction. The optimizer, loss function, activation functions, filter and kernel sizes, regularization methods, and the batch size of the proposed architecture are tuned so that the classification performance is maximized while minimizing the computational cost. Experimental results demonstrate the feasibility of the proposed method with accuracy, specificity, and kappa score of 92.53%, 95.38%, and 76.7%, respectively.Publication Metadata only HiMODE: A Hybrid Monocular Omnidirectional Depth Estimation Model(IEEE, 2022) Junayed, Masum Shah; Sadeghzadeh, Arezoo; Islam, Md Baharul; Wong, Lai-Kuan; Aydin, Tarkan; Bahcesehir University; Multimedia UniversityMonocular omnidirectional depth estimation is receiving considerable research attention due to its broad applications for sensing 360 degrees surroundings. Existing approaches in this field suffer from limitations in recovering small object details and data lost during the ground-truth depth map acquisition. In this paper, a novel monocular omnidirectional depth estimation model, namely HiMODE is proposed based on a hybrid CNN+Transformer (encoder-decoder) architecture whose modules are efficiently designed to mitigate distortion and computational cost, without performance degradation. Firstly, we design a feature pyramid network based on the HNet block to extract high-resolution features near the edges. The performance is further improved, benefiting from a self and cross attention layer and spatial/temporal patches in the Transformer encoder and decoder, respectively. Besides, a spatial residual block is employed to reduce the number of parameters. By jointly passing the deep features extracted from an input image at each backbone block, along with the raw depth maps predicted by the transformer encoder-decoder, through a context adjustment layer, our model can produce resulting depth maps with better visual quality than the ground-truth. Comprehensive ablation studies demonstrate the significance of each individual module. Extensive experiments conducted on three datasets, Stanford3D, Matterport3D, and SunCG, demonstrate that HiMODE can achieve state-of-the-art performance for 360 degrees monocular depth estimation. Complete project code and supplementary materials are available at https://github.com/himode5008/HiMODE.Publication Metadata only Deep Covariance Feature and CNN-based End-to-End Masked Face Recognition(IEEE, 2021) Junayed, Masum Shah; Sadeghzadeh, Arezoo; Islam, Md Baharul; Struc, V; Ivanovska, M; Bahcesehir UniversityWith 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.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 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.Publication Metadata only Real-Time YOLO-based Heterogeneous front vehicles detection(Institute of Electrical and Electronics Engineers Inc., 2021) Junayed, Masum Shah; Islam, Md Baharul; Sadeghzadeh, Arezoo; Aydin, Tarkan; Kilimci, Z.H.; Yildirim, T.; Piuri, V.; Czarnowski, I.; Camacho, D.; Manolopoulos, Y.; Solak, S.; Junayed, Masum Shah, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey, Daffodil International University, Dhaka, Bangladesh; Islam, Md Baharul, Bahçeşehir Üniversitesi, Istanbul, Turkey, College of Data Science and Engineering, American University of Malta, Cospicua, Malta; Sadeghzadeh, Arezoo, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Aydin, Tarkan, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, TurkeyThe perception of the complex road environment is a critical factor in autonomous driving, which has become the research focus in intelligent vehicles. In this paper, a real-time front vehicle detection system is proposed to ensure safe driving in a complex environment, particularly in congested megacities. This system is based on the YOLO model, which effectively detects and classifies various vehicles from both images and videos. It improves detection accuracy by modifying a feature extraction-based backbone. To the authors' best knowledge, this is the first time that vehicle detection is implemented on the recently published DhakaAI dataset. Compared to the other available datasets for object detection, such as KITTI, the DhakaAI dataset has a complex environment with numerous vehicles (21 different types). Experimental results demonstrate that the proposed system outperforms the state-of-the-art object detectors. In this method, the mAP (mean average precision) and the FPS (frame per second) is increased by 2.97% and 1.47, 4.64% and 5.57, 4.75% and 3.02, compared to the RetinaNet, SSD, and Faster RCNN on this dataset, respectively. © 2021 Elsevier B.V., All rights reserved.Publication Metadata only ScarNet: Development and Validation of a Novel Deep CNN Model for Acne Scar Classification with a New Dataset(Institute of Electrical and Electronics Engineers Inc., 2022) Junayed, Masum Shah; Islam, Md Baharul; Jeny, Afsana Ahsan; Sadeghzadeh, Arezoo; Biswas, Topu; Shah, A. F.M.Shahen; Junayed, Masum Shah, Department of Computer Engineering, Daffodil International University, Dhaka, Bangladesh, 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, Malta; Jeny, Afsana Ahsan, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Sadeghzadeh, Arezoo, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Biswas, Topu, Multimedia University, Cyberjaya, Malaysia; Shah, A. F.M.Shahen, Department of Electronics and Communication Engineering, Yıldız Teknik Üniversitesi, Istanbul, TurkeyAcne scarring occurs in 95% of people with acne vulgaris due to collagen loss or gains when the body is healing the damages of the skin caused by acne inflammation. Accurate classification of acne scars is a vital factor in providing a timely, effective treatment protocol. Dermatologists mainly recognize the type of acne scars manually based on visual inspections, which are time- and energy-consuming and subject to intra- and inter-reader variability. In this paper, a novel automated acne scar classification system is proposed based on a deep Convolutional Neural Network (CNN) model. First, a dataset of 250 images from five different classes is collected and labeled by four well-experienced dermatologists. The pre-processed input images are fed into our proposed model, namely ScarNet, for deep feature map extraction. The optimizer, loss function, activation functions, filter and kernel sizes, regularization methods, and the batch size of the proposed architecture are tuned so that the classification performance is maximized while minimizing the computational cost. Experimental results demonstrate the feasibility of the proposed method with accuracy, specificity, and kappa score of 92.53%, 95.38%, and 76.7%, respectively. © 2022 Elsevier B.V., All rights reserved.Publication Metadata only HiMODE: A Hybrid Monocular Omnidirectional Depth Estimation Model(IEEE Computer Society, 2022) Junayed, Masum Shah; Sadeghzadeh, Arezoo; Islam, Md Baharul; Lai-Kuan, Wong; Aydin, Tarkan; Junayed, Masum Shah, Bahçeşehir Üniversitesi, Istanbul, Turkey; Sadeghzadeh, Arezoo, Bahçeşehir Üniversitesi, Istanbul, Turkey; Islam, Md Baharul, Bahçeşehir Üniversitesi, Istanbul, Turkey, American University of Malta, Cospicua, Malta; Lai-Kuan, Wong, Multimedia University, Cyberjaya, Malaysia; Aydin, Tarkan, Bahçeşehir Üniversitesi, Istanbul, TurkeyMonocular omnidirectional depth estimation is receiving considerable research attention due to its broad applications for sensing 360° surroundings. Existing approaches in this field suffer from limitations in recovering small object details and data lost during the ground-truth depth map acquisition. In this paper, a novel monocular omnidirectional depth estimation model, namely HiMODE is proposed based on a hybrid CNN+Transformer (encoder-decoder) architecture whose modules are efficiently designed to mitigate distortion and computational cost, without performance degradation. Firstly, we design a feature pyramid network based on the HNet block to extract high-resolution features near the edges. The performance is further improved, benefiting from a self and cross attention layer and spatial/temporal patches in the Transformer encoder and decoder, respectively. Besides, a spatial residual block is employed to reduce the number of parameters. By jointly passing the deep features extracted from an input image at each backbone block, along with the raw depth maps predicted by the transformer encoder-decoder, through a context adjustment layer, our model can produce resulting depth maps with better visual quality than the ground-truth. Comprehensive ablation studies demonstrate the significance of each individual module. Extensive experiments conducted on three datasets, Stanford3D, Matterport3D, and SunCG, demonstrate that HiMODE can achieve state-of-the-art performance for 360° monocular depth estimation. Complete project code and supplementary materials are available at https://github.com/himode5008/HiMODE. © 2025 Elsevier B.V., All rights reserved.Publication Metadata only Hybrid CNN+Transformer for Diabetic Retinopathy Recognition and Grading(Institute of Electrical and Electronics Engineers Inc., 2023) Sadeghzadeh, Arezoo; Junayed, Masum Shah; Aydin, Tarkan; Islam, Md Baharul; Sadeghzadeh, Arezoo, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Junayed, Masum Shah, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Aydin, Tarkan, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Islam, Md Baharul, Bahçeşehir Üniversitesi, Istanbul, Turkey, Florida Gulf Coast University, Fort Myers, United StatesDiabetic retinopathy (DR) is a cause of blindness when it is not cured timely. Therefore, automatic DR detection and grading systems play a significant role in early diagnosis and treatment. However, the accuracy of the existing computer-aided systems is still insufficient for clinical applications and they need large-scale training datasets for obtaining good performance. This paper proposes a hybrid CNN+Transformer DR recognition and grading system to competitively improve performance even when directly trained on small datasets. Firstly, a deep CNN-based EfficientNet-B0 backbone is used as the feature extractor. Then, global dependencies are drawn between the input and output by employing a Transformer encoder-decoder (TE-TD), interleaved with Multi-Head Self Attentions (MHSA) for feature encoding. It is followed by a Residual Spatial Module (RSM) to improve the performance of the model further while stabilizing the training. A prediction feed-forward network (PFFN) is used as a classifier. The effectiveness of different modules on the performance of the system and the superiority of the combined CNN and Transformer over plain individual architectures are all investigated through comprehensive ablation studies. Our approach attains a high generalization by obtaining state-of-the-art performance in both recognition and grading on five different benchmark datasets, i.e., EyePACS, APTOS, DDR, Messidor-l, and Messidor-2. © 2023 Elsevier B.V., All rights reserved.