scholarly journals Skin disease classification versus skin lesion characterization: Achieving robust diagnosis using multi-label deep neural networks

2016 ◽  
Author(s):  
Haofu Liao ◽  
Yuncheng Li ◽  
Jiebo Luo
Keyword(s):  
Neural Networks ◽  
Skin Lesion ◽  
Skin Disease ◽  
Deep Neural Networks ◽  
Disease Classification ◽  
Lesion Characterization

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.

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.

Performance Analysis of Deep Neural Networks for Tomato Leaf Disease Classification with Server-Based Computing

2021 ◽  
Author(s):  
Kartik E. Cholachgudda ◽  
Rajashekhar C. Biradar ◽  
Kouame Yann Olivier Akansie ◽  
R. Lohith ◽  
Aras Amruth.Raj Purushotham
Keyword(s):  
Neural Networks ◽  
Performance Analysis ◽  
Deep Neural Networks ◽  
Tomato Leaf ◽  
Disease Classification ◽  
Leaf Disease

scholarly journals Lung Segmentation-Based Pulmonary Disease Classification Using Deep Neural Networks

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 125202-125214
Author(s):  
S. Zainab Yousuf Zaidi ◽  
M. Usman Akram ◽  
Amina Jameel ◽  
Norah Saleh Alghamdi
Keyword(s):  
Neural Networks ◽  
Pulmonary Disease ◽  
Deep Neural Networks ◽  
Disease Classification ◽  
Lung Segmentation
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