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Karshiev Sanjar
The School of Computer Science and Engineering, Kyungpook National University, Daegu, South Korea

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Special issue paper
Published: 04 January 2021 in Multimedia Systems
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Recently, the introduction of Convolutional Neural Network (CNNs) has advanced the way of solving image segmentation tasks. Semantic image segmentation has considerably benefited from employing various CNN models. The most widely used network in this field is U-Net and its different variations. However, these models require significant number of trainable parameters, floating-point operations per second, and great computational power to be trained. These factors make real-time semantic segmentation in low powered devices very hard. Therefore, in the present paper, we aim to modify particular aspects of the U-Net model to improve its performance through developing a fast U-Net (FU-Net) relying on bottleneck convolution layers in the contraction and expansion paths of the model. The proposed model can be utilized in semantic segmentation applications even on the devices with limited computational power and memory by ensuring the state-of-the-art performance. The amount of memory required by the proposed model is reduced by 23 times when compared with the original U-Net. Moreover, the modifications allowed achieving better performance. In conducted experiments, we assessed the performance of the proposed model on two biomedical image segmentation datasets, namely 2018 Data Science Bowl and ICIS 2018: Skin Lesion Analysis Towards Melanoma Detection. FU-Net demonstrated the state-of-the-art results in biomedical image segmentation, requiring the number of trainable parameters reduced by eight times compared with the original U-Net model. In addition, using bottleneck layers decreased the number of computations, resulting in nearly 30% speed-up at the training, validation and test stages. Furthermore, despite relying on fewer parameters FU-Net achieved a slight improvement of the performance in terms of pixel accuracy, Jaccard index, and dice coefficient evaluation metrics.

ACS Style

Bekhzod Olimov; Karshiev Sanjar; Sadia Din; Awaise Ahmad; Anand Paul; Jeonghong Kim. FU-Net: fast biomedical image segmentation model based on bottleneck convolution layers. Multimedia Systems 2021, 27, 637 -650.

AMA Style

Bekhzod Olimov, Karshiev Sanjar, Sadia Din, Awaise Ahmad, Anand Paul, Jeonghong Kim. FU-Net: fast biomedical image segmentation model based on bottleneck convolution layers. Multimedia Systems. 2021; 27 (4):637-650.

Chicago/Turabian Style

Bekhzod Olimov; Karshiev Sanjar; Sadia Din; Awaise Ahmad; Anand Paul; Jeonghong Kim. 2021. "FU-Net: fast biomedical image segmentation model based on bottleneck convolution layers." Multimedia Systems 27, no. 4: 637-650.

Journal article
Published: 25 May 2020 in Applied Sciences
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The early and accurate diagnosis of skin cancer is crucial for providing patients with advanced treatment by focusing medical personnel on specific parts of the skin. Networks based on encoder–decoder architectures have been effectively implemented for numerous computer-vision applications. U-Net, one of CNN architectures based on the encoder–decoder network, has achieved successful performance for skin-lesion segmentation. However, this network has several drawbacks caused by its upsampling method and activation function. In this paper, a fully convolutional network and its architecture are proposed with a modified U-Net, in which a bilinear interpolation method is used for upsampling with a block of convolution layers followed by parametric rectified linear-unit non-linearity. To avoid overfitting, a dropout is applied after each convolution block. The results demonstrate that our recommended technique achieves state-of-the-art performance for skin-lesion segmentation with 94% pixel accuracy and a 88% dice coefficient, respectively.

ACS Style

Karshiev Sanjar; Olimov Bekhzod; Jaeil Kim; Jaesoo Kim; Anand Paul; Jeonghong Kim. Improved U-Net: Fully Convolutional Network Model for Skin-Lesion Segmentation. Applied Sciences 2020, 10, 3658 .

AMA Style

Karshiev Sanjar, Olimov Bekhzod, Jaeil Kim, Jaesoo Kim, Anand Paul, Jeonghong Kim. Improved U-Net: Fully Convolutional Network Model for Skin-Lesion Segmentation. Applied Sciences. 2020; 10 (10):3658.

Chicago/Turabian Style

Karshiev Sanjar; Olimov Bekhzod; Jaeil Kim; Jaesoo Kim; Anand Paul; Jeonghong Kim. 2020. "Improved U-Net: Fully Convolutional Network Model for Skin-Lesion Segmentation." Applied Sciences 10, no. 10: 3658.

Journal article
Published: 08 April 2020 in ISPRS International Journal of Geo-Information
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Accurate house price forecasts are very important for formulating national economic policies. In this paper, we offer an effective method to predict houses’ sale prices. Our algorithm includes one-hot encoding to convert text data into numeric data, feature correlation to select only the most correlated variables, and a technique to overcome the missing data. Our approach is an effective way to handle missing data in large datasets with the K-nearest neighbor algorithm based on the most correlated features (KNN–MCF). As far as we are concerned, there has been no previous research that has focused on important features dealing with missing observations. Compared to the typical machine learning prediction algorithms, the prediction accuracy of the proposed method is 92.01% with the random forest algorithm, which is more efficient than the other methods.

ACS Style

Karshiev Sanjar; Olimov Bekhzod; Jaesoo Kim; Anand Paul; Jeonghong Kim. Missing Data Imputation for Geolocation-based Price Prediction Using KNN–MCF Method. ISPRS International Journal of Geo-Information 2020, 9, 227 .

AMA Style

Karshiev Sanjar, Olimov Bekhzod, Jaesoo Kim, Anand Paul, Jeonghong Kim. Missing Data Imputation for Geolocation-based Price Prediction Using KNN–MCF Method. ISPRS International Journal of Geo-Information. 2020; 9 (4):227.

Chicago/Turabian Style

Karshiev Sanjar; Olimov Bekhzod; Jaesoo Kim; Anand Paul; Jeonghong Kim. 2020. "Missing Data Imputation for Geolocation-based Price Prediction Using KNN–MCF Method." ISPRS International Journal of Geo-Information 9, no. 4: 227.