Automatic Diagnosis of Skin Cancer Using Neural Networks

2019 ◽  
Author(s):  
Serban Radu Stefan Jianu ◽  
Loretta Ichim ◽  
Dan Popescu
Keyword(s):  
Neural Networks ◽  
Skin Cancer ◽  
Automatic Diagnosis

scholarly journals Deep convolutional neural networks for COVID‐19 automatic diagnosis

2021 ◽  
Author(s):  
Heba M. Emara ◽  
Mohamed R. Shoaib ◽  
Mohamed Elwekeil ◽  
Walid El‐Shafai ◽  
Taha E. Taha ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Automatic Diagnosis

Computerized diagnosis of skin cancer using neural networks

1996 ◽  
Vol 6 (SUPPLEMENT 1) ◽  
pp. S30
Author(s):  
R. Husemann ◽  
S. T??lg ◽  
K. Hoffmann ◽  
W. von Seelen ◽  
P. Altmeyer ◽  
...  
Keyword(s):  
Neural Networks ◽  
Skin Cancer

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.

scholarly journals Skin Lesion Classification Based on Deep Convolutional Neural Networks Architectures

2021 ◽  
Vol 2 (01) ◽  
pp. 41-51
Author(s):  
Jwan Saeed ◽  
Subhi Zeebaree
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Skin Cancer ◽  
Cancer Detection ◽  
Deep Convolutional Neural Networks ◽  
Color Structure ◽  
Malignant Skin ◽  
Dermatological Disorders ◽  
Detection And Diagnosis ◽  
Skin Cancer Detection

Skin cancer is among the primary cancer types that manifest due to various dermatological disorders, which may be further classified into several types based on morphological features, color, structure, and texture. The mortality rate of patients who have skin cancer is contingent on preliminary and rapid detection and diagnosis of malignant skin cancer cells. Limitations in current dermoscopic images, including shadow, artifact, and noise, affect image quality, which may hamper detection effort. Attempts to overcome these challenges have been made by analyzing the images using deep learning neural networks to perform skin cancer detection. In this paper, the authors review the state-of-the-art in authoritative deep learning concepts pertinent to skin cancer detection and classification.

scholarly journals Erratum: Corrigendum: Dermatologist-level classification of skin cancer with deep neural networks

Nature ◽  
2017 ◽  
Vol 546 (7660) ◽  
pp. 686-686 ◽  
Author(s):  
Andre Esteva ◽  
Brett Kuprel ◽  
Roberto A. Novoa ◽  
Justin Ko ◽  
Susan M. Swetter ◽  
...  
Keyword(s):  
Neural Networks ◽  
Skin Cancer ◽  
Deep Neural Networks

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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