scholarly journals Skin Cancer Classification Using Convolutional Neural Networks

2021 ◽  
Author(s):  
Trisha Raj
Keyword(s):  
Neural Networks ◽  
Skin Cancer ◽  
Convolutional Neural Networks ◽  
Cancer Classification

Skin cancer classification using Convolutional neural networks

2021 ◽  
Author(s):  
R Raja Subramanian ◽  
Dintakurthi Achuth ◽  
P Shiridi Kumar ◽  
Kovvuru Naveen kumar Reddy ◽  
Srikar Amara ◽  
...  
Keyword(s):  
Neural Networks ◽  
Skin Cancer ◽  
Convolutional Neural Networks ◽  
Cancer Classification

scholarly journals Skin Cancer Classification Using Convolutional Neural Networks with Integrated Patient Data: A Systematic Review (Preprint)

10.2196/20708 ◽  
2020 ◽  
Author(s):  
Julia Höhn ◽  
Achim Hekler ◽  
Eva Krieghoff-Henning ◽  
Jakob Nikolas Kather ◽  
Jochen Sven Utikal ◽  
...  
Keyword(s):  
Systematic Review ◽  
Neural Networks ◽  
Skin Cancer ◽  
Convolutional Neural Networks ◽  
Patient Data ◽  
Cancer Classification

scholarly journals Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts

2021 ◽  
Vol 156 ◽  
pp. 202-216 ◽  
Author(s):  
Sarah Haggenmüller ◽  
Roman C. Maron ◽  
Achim Hekler ◽  
Jochen S. Utikal ◽  
Catarina Barata ◽  
...  
Keyword(s):  
Systematic Review ◽  
Neural Networks ◽  
Skin Cancer ◽  
Convolutional Neural Networks ◽  
Cancer Classification

scholarly journals Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review

10.2196/11936 ◽  
2018 ◽  
Vol 20 (10) ◽  
pp. e11936 ◽  
Author(s):  
Titus Josef Brinker ◽  
Achim Hekler ◽  
Jochen Sven Utikal ◽  
Niels Grabe ◽  
Dirk Schadendorf ◽  
...  
Keyword(s):  
Systematic Review ◽  
Neural Networks ◽  
Skin Cancer ◽  
Convolutional Neural Networks ◽  
Cancer Classification

scholarly journals Convolutional Neural Networks for Classifying Melanoma Images

2020 ◽  
Author(s):  
Abhinav Sagar ◽  
J Dheeba
Keyword(s):  
Neural Networks ◽  
Skin Cancer ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Cancer Classification ◽  
Previous State ◽  
Better Than

AbstractIn this work, we address the problem of skin cancer classification using convolutional neural networks. A lot of cancer cases early on are misdiagnosed as something else leading to severe consequences including the death of a patient. Also there are cases in which patients have some other problems and doctors think they might have skin cancer. This leads to unnecessary time and money spent for further diagnosis. In this work, we address both of the above problems using deep neural networks and transfer learning architecture. We have used publicly available ISIC databases for both training and testing our model. Our work achieves an accuracy of 0.935, precision of 0.94, recall of 0.77, F1 score of 0.85 and ROC-AUC of 0.861 which is better than the previous state of the art approaches.

scholarly journals Convolutional Neural Networks Model in Premature Detection of Melanoma

2019 ◽  
Vol 8 (12S2) ◽  
pp. 6-11 ◽  
Keyword(s):  
Neural Networks ◽  
Skin Cancer ◽  
Convolutional Neural Networks ◽  
Additional Data ◽  
Data Set ◽  
Non Invasive ◽  
Convolution Filter ◽  
The University ◽  
Improved Accuracy

The objective of this research is provide to the specialists in skin cancer, a premature, rapid and non-invasive diagnosis of melanoma identification, using an image of the lesion, to apply to the treatment of a patient, the method used is the architecture contrast of Convolutional neural networks proposed by Laura Kocobinski of the University of Boston, against our architecture, which reduce the depth of the convolution filter of the last two convolutional layers to obtain maps of more significant characteristics. The performance of the model was reflected in the accuracy during the validation, considering the best result obtained, which is confirmed with the additional data set. The findings found with the application of this base architecture were improved accuracy from 0.79 to 0.83, with 30 epochs, compared to Kocobinski's AlexNet architecture, it was not possible to improve the accuracy of 0.90, however, the complexity of the network played an important role in the results we obtained, which was able to balance and obtain better results without increasing the epochs, the application of our research is very helpful for doctors, since it will allow them to quickly identify if an injury is melanoma or not and consequently treat it efficiently.

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