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Dual u-net with resnet encoder for segmentation of medical images by Syed, Qamrun Nisa, Ismail, Amelia Ritahani

“…The algorithms used in this paper for segmentation of gastrointestinal polyp images depend on profound deep convolutional neural network architectures: FCN, Dual U-net with Resnet Encoder, U-net, and Unet_Resnet. …”
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Diabetic Retinopathy Detection Model using Hybrid of U-Net and Vision Transformer Algorithms by Mudit, Khater

“…Now, we present a hybrid model which is a combination of U-Net algorithm used for image segmentation and Vision Transformer for classification. …”
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Segmentation and location computation of bin objects by Hema, Chengalvarayan Radhakrishnamurthy, Paulraj, Murugesapandian, Ramachandran, Nagarajan, Sazali, Yaacob

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Brain lesion image segmentation using modified U-NET architecture by Lee, Xin Yin, Mohd Jamil, Mohamed Mokhtarudin, Ramli, Junid

“…In this project, a brain stroke lesion segmentation algorithm using a modified U-Net (MUN) architecture will be developed. …”
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Deep learning segmentation of brain ischemic lesion from magnetic resonance images for three-dimensional modelling by Muhammad Ismaill, Mat Lizah, Nasrul Hadi, Johari, Mohd Jamil, Mohamed Mokhtarudin

“…This work aims to develop a computationally economical automated brain infarct segmentation from T1-weighted Magnetic Resonance Imaging (MRI) using convolutional neural network architecture U-Net, but with reasonable accuracy compared to existing algorithm. …”
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Application of Fuzzy C-Means with YCbCr and DenseNet-201 for Automated Corn Leaf Disease Detection by Chyntia Jaby, Entuni

“…This is due to instability and complexity of the network. Hence, algorithm that performed better is required. Thus, in this study, image segmentation method of Fuzzy C-Means with YCbCr and image classification method of DenseNet-201 to detect plant leaf diseases is proposed. …”
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Deep Learning Based image segmentation for expensive soil desiccation crack recognition and qualification by Ling, Hui Yean

“…Deep learning models, including U-Net, Res-UNet, and DeepLabv3+ with pre-trained backbones such as MobileNetV2, ResNet-18, ResNet-50, and Xception, were trained and evaluated along side a traditional Otsu's thresholding method as the baseline for crack detection and segmentation. …”
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Automated visual defect detection using deep learning by Loh, Xiao

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Automatic Segmentation and Classification of Skin Lesions in Dermoscopic Images by Adil Humayun, Khan

“…The third proposed segmentation algorithm involves two pipelines for feature extraction: split & merge methods and Contextual Encoding Network (EncNet) with an attention mechanism. …”
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Blood cell image segmentation using unsupervised clustering techniques by Tuan Muda, Tuan Zalizam, Abdul Salam, Rosalina

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Analysis of breast region segmentation in thermal images using U-Net deep neural network variants by Rosli, Rafhanah Shazwani, Habaebi, Mohamed Hadi, Islam, Md. Rafiqul, Al Husaini, Mohammed Abdulla Salim

“…Methods: This study evaluated three segmentation models—U-Net, U-Net with Spatial Attention, and U-Net++—using five optimization algorithms (ADAM, NADAM, RMSPROP, SGDM, and ADADELTA). …”
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Development of brain tumor segmentation of magnetic resonance imaging (MRI) using u-net deep learning by Jwaid W.M., Al-Hussein Z.S.M., Sabry A.H.

“…The study built and trained the 3D U-Net CNN including encoding/decoding relationship architecture to perform the brain tumor segmentation because it requires fewer training images and provides more precise segmentation. …”

Performance comparison between RGB and HSV color segmentations for road signs detection by Mohd Ali, Nursabillilah, Mohd Mustafah, Yasir, Alang Md Rashid, Nahrul Khair

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2TSS: Two-tier semantic segmentation framework with enhancement for hotspot detection of solar photovoltaic thermal images by Nurul Huda, Ishak, Iza Sazanita, Isa, Muhammad Khusairi, Osman, Mohd Shawal, Jadin, Kamarulazhar, Daud, Mohd Zulhamdy, Ab Hamid

“…Three different segmentation models, namely U-Net, ResNet 18 and ResNet 50 were tested. …”
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AUTOMATED CERVICAL CELL NUCLEI SEGMENTATION BASED ON MULTILAYER UNSUPERVISED CLUSTERING ALGORITHM AND MORPHOLOGICAL APPROACH by Khalis Danial Nukman, Khiruddin, Wan Azani, Mustafa, Khairur Rijal, Jamaludin, Khairul Shakir, Ab Rahman, Hiam, Alquran, Syahrul Nizam, Junaini

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Deep learning-based water segmentation for autonomous surface vessel by Mohd Adam, Muhammad Ammar, Ibrahim, Ahmad Imran, Zainal Abidin, Zulkifli, Mohd Zaki, Hasan Firdaus

“…To improve, pre-trained networks (VGG 16, Resnet-50 and MobileNet) are used as a backbone, obtaining an improved accuracy above 98.14% with lowest inferring speed of 10 frame per second. …”
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Development of seven segment display recognition using TensorFlow on Raspberry Pi by Gunawan, Teddy Surya, Mohammad Reduwan, Haque, Yaacob, Mashkuri, Mohamed Azmin, Nor Fadhillah, Ismail, Nanang, Ismail, Md Yusof, Kartiwi, Mira, A. Rahman, Azhar

“…SSD-MobileNet will be used for subsequent experiments, out of the two architectures evaluated, Tiny YOLO and SSD-MobileNet. …”
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Improving brain tumor segmentation in MRI images through enhanced convolutional neural networks by Ayomide, Kabirat Sulaiman, Mohd Aris, Teh Noranis, Zolkepli, Maslina

“…The research focuses on enhancing brain tumor segmentation in MRI images by using Convolutional Neural Networks and reducing training time by using MATLAB's GoogLeNet, anisotropic diffusion filtering, morphological operation, and sector vector machine for MRI images. …”
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A Reinforcement Learning Based Adaptive ROI Generation for Video Object Segmentation by Usmani, U.A., Watada, J., Jaafar, J., Aziz, I.A., Roy, A.

“…The proposed method has been validated using four commonly used video entity segmentation datasets: SegTrack V2, DAVIS 2016, CdNet 2014, and the Youtube-Object dataset. …”
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Deep learning semantic segmentation for water level estimation using surveillance camera by Muhadi, Nur 'Atirah, Abdullah, Ahmad Fikri, Bejo, Siti Khairunniza, Mahadi @ Othman, Muhammad Razif, Mijic, Ana

“…This work presented two well-established deep learning algorithms, DeepLabv3+ and SegNet networks, and evaluated their performances using several evaluation metrics. …”
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