Effect on model performance of regularization methods

Budak, Cafer; Mençik, Vasfiye; Asker, Mehmet Emin

doi:10.24012/dumf.1051352

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…The final layer maps all components of the final feature map into two classes which need to be estimated by a softmax activation function. In the U-Net models used, Batch Normalization [39] and Dropout [40] were used together to prevent overfitting at each layer. Dropout is based on the principle of ignoring some randomly selected neurons during training.…”

Section: U-net Architecturementioning

confidence: 99%

Biomedical Image Segmentation with Modified U-Net

Tatli,

Budak

2023

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Image segmentation is an important field in image processing and computer vision, particularly in the development of methods to assist experts in the biomedical and medical fields. It plays a vital role in saving time and costs. One of the most successful and significant methods in image segmentation using deep learning is the U-Net model. In this paper, we propose U-Net11, a novel variant of U-Net that uses 11 convolutional layers and introduces some modifications to improve the segmentation performance. The classical U-Net model was developed and tested on three different datasets, outperforming the traditional U-Net approach. The U-Net11 model was evaluated for breast cancer segmentation, lung segmentation from CT images, and the nuclei segmentation dataset from the Data Science Bowl 2018 competition. These datasets are valuable due to their varying image quantities and the varying difficulty levels in segmentation tasks. The modified U-Net model has achieved Dice Similarity Coefficient scores of 69.09% on the breast cancer dataset, 95.02% on the lung segmentation dataset and 81.10% on the nuclei segmentation dataset, exceeding the performance of the classical U-Net model by 5%, 2% and 4% respectively. This difference in success rates is particularly significant for critical segmentation datasets.deep learning,

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Section: U-net Architecturementioning

confidence: 99%

Biomedical Image Segmentation with Modified U-Net

Tatli,

Budak

2023

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“…Also, SAE and DBN are unsupervised learning algorithms and do not directly use informations of class while learning features [21]. Convolutional neural networks (CNN) are another deep learning method used for HSIC [22]. CNN has a strong feature learning ability that can ensure more distinctive features for higher quality HSIC [23].…”

mentioning

confidence: 99%

Hybrid 3D Convolution and 2D Depthwise Separable Convolution Neural Network for Hyperspectral Image Classification

FIRAT

Asker

Hanbay

2022

Balkan Journal of Electrical and Computer Engineering

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Convolutional neural networks (CNNs) are one of deep learning methods that are often used to solve the problem of hyperspectral image classification (HSIC). CNN has a strong feature learning ability that can ensure more distinctive features for higher quality HSIC. The traditional CNN-based methods mainly use the 2D CNN for HSIC. However, with 2D CNN, only spatial features are extracted in HSI. Good feature maps cannot be extracted from spectral dimensions with the use of 2D CNN alone. By using 3D CNN, spatial-spectral features are extracted simultaneously. However, 3D CNN is computationally complex. In this study, a hybrid CNN method, which is a combination of 3D CNN and 2D CNN, is improved to solve the two problems described above. Using hybrid CNN decreases the complexity of the method compared to using only 3D CNN and can perform well against a limited number of training samples. On the other hand, in Hybrid CNN, depthwise separable convolution (DSC) is used, which decreases computational cost, prevents overfitting and enables more spatial feature extraction. By adding DSC to the developed hybrid CNN, a hybrid depthwise separable convolutional neural network is obtained. Extensive applications on frequently used HSI benchmark datasets show that the classification performance of the proposed network is better than compared methods.

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Effect on model performance of regularization methods

Cited by 2 publications

References 19 publications

Biomedical Image Segmentation with Modified U-Net

Biomedical Image Segmentation with Modified U-Net

Hybrid 3D Convolution and 2D Depthwise Separable Convolution Neural Network for Hyperspectral Image Classification

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