Gastric Lesion Classification Using Deep Learning Based on Fast and Robust Fuzzy C-Means and Simple Linear Iterative Clustering Superpixel Algorithms

2019 ◽  
Vol 14 (6) ◽  
pp. 2549-2556
Author(s):  
Dong-hyun Kim ◽  
HyunChin Cho ◽  
Hyun-chong Cho
Keyword(s):  
Deep Learning ◽  
Gastric Lesion ◽  
Fuzzy C Means ◽  
Simple Linear Iterative Clustering ◽  
Lesion Classification

scholarly journals Improving Colonoscopy Lesion Classification Using Semi-Supervised Deep Learning

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 631-640
Author(s):  
Mayank Golhar ◽  
Taylor L. Bobrow ◽  
Mirmilad Pourmousavi Khoshknab ◽  
Simran Jit ◽  
Saowanee Ngamruengphong ◽  
...  
Keyword(s):  
Deep Learning ◽  
Lesion Classification

Wavelet-based deep learning for skin lesion classification

2020 ◽  
Vol 14 (4) ◽  
pp. 720-726 ◽  
Author(s):  
Sertan Serte ◽  
Hasan Demirel
Keyword(s):  
Deep Learning ◽  
Skin Lesion ◽  
Lesion Classification

Deep learning for extracting water body from Sentinel-2 MSI imagery

2021 ◽  
Author(s):  
Shuren Chou
Keyword(s):  
Deep Learning ◽  
Distance Measure ◽  
Accuracy Assessment ◽  
Feature Learning ◽  
Search Space ◽  
Water Bodies ◽  
Detection Accuracy ◽  
Simple Linear Iterative Clustering ◽  
Clustering Center ◽  
Sentinel 2

<p>Deep learning has a good capacity of hierarchical feature learning from unlabeled remote sensing images. In this study, the simple linear iterative clustering (SLIC) method was improved to segment the image into good quality super-pixels. Then, we used the convolutional neural network (CNN) to extract of water bodies from Sentinel-2 MSI data using deep learning technique. In the proposed framework, the improved SLIC method obtained the correct water bodies boundary by optimizing the initial clustering center, designing a dynamic distance measure, and expanding the search space. In addition, it is different from traditional extraction of water bodies methods that cannot achieve multi-level water bodies detection. Experimental results showed that this method had higher detection accuracy and robustness than other methods. This study was able to extract water bodies from remotely sensed images with deep learning and to conduct accuracy assessment.</p>

scholarly journals Combining DBN and FCM for Fault Diagnosis of Roller Element Bearings without Using Data Labels

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Fan Xu ◽  
Yan jun Fang ◽  
Dong Wang ◽  
Jia qi Liang ◽  
Kwok Leung Tsui
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Clustering Algorithm ◽  
Principal Component ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
Vibration Signals ◽  
Fuzzy C Means ◽  
Roller Bearings ◽  
Mode Decomposition

Because deep belief networks (DBNs) in deep learning have a powerful ability to extract useful information from the raw data without prior knowledge, DBNs are used to extract the useful feature from the roller bearings vibration signals. Unlike classification methods, the clustering method can classify the different fault types without data label. Therefore, a method based on deep belief networks (DBNs) in deep learning (DL) and fuzzy C-means (FCM) clustering algorithm for roller bearings fault diagnosis without a data label is presented in this paper. Firstly, the roller bearings vibration signals are extracted by using DBN, and then principal component analysis (PCA) is used to reduce the dimension of the vibration signal features. Secondly, the first two principal components (PCs) are selected as the input of fuzzy C-means (FCM) for roller bearings fault identification. Finally, the experimental results show that the fault diagnosis of the method presented is better than that of other combination models, such as variation mode decomposition- (VMD-) singular value decomposition- (SVD-) FCM, and ensemble empirical mode decomposition- (EEMD-) fuzzy entropy- (FE-) PCA-FCM.

scholarly journals Scalable Simple Linear Iterative Clustering (SSLIC) Using a Generic and Parallel Approach

2018 ◽  
Author(s):  
Bradley Lowekamp ◽  
David Chen ◽  
Ziv Yaniv ◽  
Terry Yoo
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Computational Complexity ◽  
Quantitative Evaluation ◽  
Initial Step ◽  
3D Image ◽  
Strong Scaling ◽  
Simple Linear Iterative Clustering ◽  
Traditional Image ◽  
Visible Human

Superpixel algorithms have proven to be a useful initial step for segmentation and subsequent processing of images, reducing computational complexity by replacing the use of expensive per-pixel primitives with a higher-level abstraction, superpixels. They have been successfully applied both in the context of traditional image analysis and deep learning based approaches. In this work, we present a general- ized implementation of the simple linear iterative clustering (SLIC) superpixel algorithm that has been generalized for n-dimensional scalar and multi-channel images. Additionally, the standard iterative im- plementation is replaced by a parallel, multi-threaded one. We describe the implementation details and analyze its scalability using a strong scaling formulation. Quantitative evaluation is performed using a 3D image, the Visible Human cryosection dataset, and a 2D image from the same dataset. Results show good scalability with runtime gains even when using a large number of threads that exceeds the physical number of available cores (hyperthreading).

scholarly journals Multimodal Multi-tasking for Skin Lesion Classification Using Deep Neural Networks

2021 ◽  
pp. 27-38
Author(s):  
Rafaela Carvalho ◽  
João Pedrosa ◽  
Tudor Nedelcu
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Skin Lesion ◽  
Deep Neural Networks ◽  
Classification Performance ◽  
Additional Information ◽  
Deep Learning Model ◽  
Lesion Classification

AbstractSkin cancer is one of the most common types of cancer and, with its increasing incidence, accurate early diagnosis is crucial to improve prognosis of patients. In the process of visual inspection, dermatologists follow specific dermoscopic algorithms and identify important features to provide a diagnosis. This process can be automated as such characteristics can be extracted by computer vision techniques. Although deep neural networks can extract useful features from digital images for skin lesion classification, performance can be improved by providing additional information. The extracted pseudo-features can be used as input (multimodal) or output (multi-tasking) to train a robust deep learning model. This work investigates the multimodal and multi-tasking techniques for more efficient training, given the single optimization of several related tasks in the latter, and generation of better diagnosis predictions. Additionally, the role of lesion segmentation is also studied. Results show that multi-tasking improves learning of beneficial features which lead to better predictions, and pseudo-features inspired by the ABCD rule provide readily available helpful information about the skin lesion.

Sign in / Sign up

Export Citation Format

Share Document