Prediction of Skin Cancer Based on Convolutional Neural Network

2018 ◽  
pp. 1223-1229
Author(s):  
Jianfeng He ◽  
Qingqing Dong ◽  
Sanli Yi
Keyword(s):  
Neural Network ◽  
Skin Cancer ◽  
Convolutional Neural Network

scholarly journals Epoch interrogation for skin cancer detection using convolutional neural network models

2021 ◽  
Vol 1074 (1) ◽  
pp. 012025
Author(s):  
A Poornima ◽  
M Shyamala Devi ◽  
M Sumithra ◽  
Mullaguri Venkata Bharath ◽  
Swathi ◽  
...  
Keyword(s):  
Neural Network ◽  
Skin Cancer ◽  
Convolutional Neural Network ◽  
Cancer Detection ◽  
Network Models ◽  
Neural Network Models ◽  
Skin Cancer Detection

scholarly journals Skin Lesion Classification With Deep Convolutional Neural Network: Process Development and Validation

10.2196/18438 ◽  
2020 ◽  
Vol 3 (1) ◽  
pp. e18438
Author(s):  
Arnab Ray ◽  
Aman Gupta ◽  
Amutha Al
Keyword(s):  
Neural Network ◽  
Skin Cancer ◽  
Skin Lesion ◽  
Convolutional Neural Network ◽  
Process Development ◽  
Early Stage ◽  
Deep Convolutional Neural Network ◽  
Fine Tuning ◽  
Network Process ◽  
Lesion Classification

Background Skin cancer is the most common cancer and is often ignored by people at an early stage. There are 5.4 million new cases of skin cancer worldwide every year. Deaths due to skin cancer could be prevented by early detection of the mole. Objective We propose a skin lesion classification system that has the ability to detect such moles at an early stage and is able to easily differentiate between a cancerous and noncancerous mole. Using this system, we would be able to save time and resources for both patients and practitioners. Methods We created a deep convolutional neural network using an Inceptionv3 and DenseNet-201 pretrained model. Results We found that using the concepts of fine-tuning and the ensemble learning model yielded superior results. Furthermore, fine-tuning the whole model helped models converge faster compared to fine-tuning only the top layers, giving better accuracy overall. Conclusions Based on our research, we conclude that deep learning algorithms are highly suitable for classifying skin cancer images.

scholarly journals Detection of Skin Cancer using Deep CNN

2020 ◽  
Vol 8 (5S) ◽  
pp. 22-24
Keyword(s):  
Neural Network ◽  
Skin Cancer ◽  
Convolutional Neural Network ◽  
The Body ◽  
The Sun ◽  
Layer System ◽  
Data Set ◽  
Abnormal Cells ◽  
Deep Cnn ◽  
Uv Rays

Development of abnormal cells are the cause of skin cancer that have the ability to attack or spread to various parts of the body. The skin cancer signs may include mole that has varied in size, shape, color, and may also haveno –uniform edges, might be having multiple colours, and would itch orevn bleed in some cases. The exposure to the UV-rays from the sun is considered to be accountable for more than 90% of the Skin Cancer cases which are recorded.In this paper, the development of a classificiation system for skin cancer, is discussed, using Convolutional Neural Network which would help in classifying the cancer usingTensorFlow and Keras as Malignantor Benign. The collected images from the data set are fed into the system and it is processed to classify the skin cancer. After the implementation the accuracy of the Convolutional 2-D layer system developed is found to be 78%.

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