scholarly journals Texture Based Automated Segmentation of Skin Lesions using Echo State Neural Networks

2017 ◽  
Vol 12 (1) ◽  
pp. 436-442 ◽  
Author(s):  
Z. Faizal Khan ◽  
Nalinipriya Ganapathi
Keyword(s):  
Neural Networks ◽  
Skin Lesions ◽  
Automated Segmentation

scholarly journals Robustness of convolutional neural networks in recognition of pigmented skin lesions

2021 ◽  
Vol 145 ◽  
pp. 81-91
Author(s):  
Roman C. Maron ◽  
Sarah Haggenmüller ◽  
Christof von Kalle ◽  
Jochen S. Utikal ◽  
Friedegund Meier ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Skin Lesions ◽  
Pigmented Skin Lesions

Automated segmentation of skin lesions: Modified Fuzzy C mean thresholding based level set method

INMIC ◽  
2013 ◽  
Author(s):  
Ammara Masood ◽  
Adel Ali Al Jumaily ◽  
Azadeh Noori Hoshyar ◽  
Omama Masood
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Skin Lesions ◽  
Automated Segmentation

Automated segmentation of the knee for age assessment in 3D MR images using convolutional neural networks

2018 ◽  
Vol 133 (4) ◽  
pp. 1191-1205 ◽  
Author(s):  
Paul-Louis Pröve ◽  
Eilin Jopp-van Well ◽  
Ben Stanczus ◽  
Michael M. Morlock ◽  
Jochen Herrmann ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Automated Segmentation ◽  
Mr Images ◽  
Age Assessment

scholarly journals SELECTED APPLICATIONS OF DEEP NEURAL NETWORKS IN SKIN LESION DIAGNOSTIC

2021 ◽  
Vol 11 (4) ◽  
pp. 18-21
Author(s):  
Magdalena Michalska
Keyword(s):  
Neural Networks ◽  
Malignant Melanoma ◽  
Skin Lesion ◽  
Deep Neural Networks ◽  
Skin Lesions ◽  
Multiclass Classification ◽  
Features Selection ◽  
Lesion Segmentation ◽  
Dermatological Diseases ◽  
Segmentation Process

The article provides an overview of selected applications of deep neural networks in the diagnosis of skin lesions from human dermatoscopic images, including many dermatological diseases, including very dangerous malignant melanoma. The lesion segmentation process, features selection and classification was described. Application examples of binary and multiclass classification are given. The described algorithms have been widely used in the diagnosis of skin lesions. The effectiveness, specificity, and accuracy of classifiers were compared and analysed based on available datasets.

Automated segmentation of macular edema in OCT using deep neural networks

2019 ◽  
Vol 55 ◽  
pp. 216-227 ◽  
Author(s):  
Junjie Hu ◽  
Yuanyuan Chen ◽  
Zhang Yi
Keyword(s):  
Neural Networks ◽  
Macular Edema ◽  
Deep Neural Networks ◽  
Automated Segmentation

scholarly journals Deep Learning–Based Methods for Automatic Diagnosis of Skin Lesions

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1753 ◽  
Author(s):  
Hassan El-Khatib ◽  
Dan Popescu ◽  
Loretta Ichim
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Classical Method ◽  
Skin Lesions ◽  
Support Vector ◽  
Dice Similarity Coefficient ◽  
Final Decision ◽  
Decision System ◽  
The One ◽  
Sensitivity Specificity

The main purpose of the study was to develop a high accuracy system able to diagnose skin lesions using deep learning–based methods. We propose a new decision system based on multiple classifiers like neural networks and feature–based methods. Each classifier (method) gives the final decision system a certain weight, depending on the calculated accuracy, helping the system make a better decision. First, we created a neural network (NN) that can differentiate melanoma from benign nevus. The NN architecture is analyzed by evaluating it during the training process. Some biostatistic parameters, such as accuracy, specificity, sensitivity, and Dice coefficient are calculated. Then, we developed three other methods based on convolutional neural networks (CNNs). The CNNs were pre-trained using large ImageNet and Places365 databases. GoogleNet, ResNet-101, and NasNet-Large, were used in the enumeration order. CNN architectures were fine-tuned in order to distinguish the different types of skin lesions using transfer learning. The accuracies of the classifications were determined. The last proposed method uses the classical method of image object detection, more precisely, the one in which some features are extracted from the images, followed by the classification step. In this case, the classification was done by using a support vector machine. Just as in the first method, the sensitivity, specificity, Dice similarity coefficient and accuracy are determined. A comparison of the obtained results from all the methods is then done. As mentioned above, the novelty of this paper is the integration of these methods in a global fusion-based decision system that uses the results obtained by each individual method to establish the fusion weights. The results obtained by carrying out the experiments on two different free databases shows that the proposed system offers higher accuracy results.

Improved automated segmentation of human kidney organoids using deep convolutional neural networks

2020 ◽  
Author(s):  
Michael MacDonald ◽  
Randy Fennel ◽  
Asha Singanamalli ◽  
Nelly Cruz ◽  
Mohammad YousefHussein ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Human Kidney ◽  
Automated Segmentation ◽  
Deep Convolutional Neural Networks ◽  
Kidney Organoids
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