scholarly journals An Improved CNN Architecture to Diagnose Skin Cancer in Dermoscopic Images Based on Wildebeest Herd Optimization Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Biying Zhou ◽  
Behdad Arandian
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Feature Selection ◽  
Skin Cancer ◽  
Cancer Diagnosis ◽  
Time Complexity ◽  
Metaheuristic Algorithm ◽  
Cancer Dataset ◽  
Popular Method ◽  
Computer Aided

Skin cancer is one of the most common types of cancers that is sometimes difficult for doctors and experts to diagnose. The noninvasive dermatoscopic method is a popular method for observing and diagnosing skin cancer. Because this method is based on ocular inference, the skin cancer diagnosis by the dermatologists is difficult, especially in the early stages of the disease. Artificial intelligence is a proper complementary tool that can be used alongside the experts to increase the accuracy of the diagnosis. In the present study, a new computer-aided method has been introduced for the diagnosis of the skin cancer. The method is designed based on combination of deep learning and a newly introduced metaheuristic algorithm, namely, Wildebeest Herd Optimization (WHO) Algorithm. The method uses an Inception convolutional neural network for the initial features’ extraction. Afterward, the WHO algorithm has been employed for selecting the useful features to decrease the analysis time complexity. The method is then performed to an ISIC-2008 skin cancer dataset. Final results of the feature selection based on the proposed WHO are compared with three other algorithms, and the results have indicated good results for the system. Finally, the total diagnosis system has been compared with five other methods to indicate its effectiveness against the studied methods. Final results showed that the proposed method has the best results than the comparative methods.

scholarly journals Cloud based mobile solution for early detection of Skin Cancer using Artificial Intelligence

2021 ◽  
pp. 312-324
Author(s):  
Pawan Sonawane ◽  
Sahel Shardhul ◽  
Raju Mendhe
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Skin Cancer ◽  
Early Detection ◽  
Transfer Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Mobile Application ◽  
Low Cost ◽  
Skin Tumors

The vast majority of skin cancer deaths are from melanoma, with about 1.04 million cases annually. Early detection of the same can be immensely helpful in order to try to cure it. But most of the diagnosis procedures are either extremely expensive or not available to a vast majority, as these centers are concentrated in urban regions only. Thus, there is a need for an application that can perform a quick, efficient, and low-cost diagnosis. Our solution proposes to build a server less mobile application on the AWS cloud that takes the images of potential skin tumors and classifies it as either Malignant or Benign. The classification would be carried out using a trained Convolution Neural Network model and Transfer learning (Inception v3). Several experiments will be performed based on Morphology and Color of the tumor to identify ideal parameters.

scholarly journals Deriving Rules for Forecasting Air Carrier Financial Stress and Insolvency: A Genetic Algorithm Approach

2010 ◽  
Author(s):  
Sergio Davalos ◽  
Richard Gritta ◽  
Bahram Adrangi
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Feature Selection ◽  
Dimensionality Reduction ◽  
Local Optima ◽  
Air Carrier ◽  
Genetic Algorithm Approach ◽  
Genetic Algorithm Model

Statistical and artificial intelligence methods have successfully classified organizational solvency, but are limited in terms of generalization, knowledge on how a conclusion was reached, convergence to a local optima, or inconsistent results. Issues such as dimensionality reduction and feature selection can also affect a model's performance. This research explores the use of the genetic algorithm that has the advantages of the artificial neural network but without its limitations. The genetic algorithm model resulted in a set of easy to understand, if-then rules that were used to assess U.S. air carrier solvency with a 94% accuracy.

Neural network feature selection for breast cancer diagnosis

1995 ◽  
Author(s):  
Catherine M. Kocur ◽  
Steven K. Rogers ◽  
Kenneth W. Bauer, Jr. ◽  
Jean M. Steppe ◽  
Jeffrey W. Hoffmeister
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Feature Selection ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Network Feature ◽  
Selection For

scholarly journals Classification of skin cancer images using TensorFlow and inception v3

2018 ◽  
Vol 7 (2.7) ◽  
pp. 717 ◽  
Author(s):  
Bhavya Sai V ◽  
Narasimha Rao G ◽  
Ramya M ◽  
Sujana Sree Y ◽  
Anuradha T
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Skin Cancer ◽  
Convolution Neural Network ◽  
Cancer Dataset ◽  
Machine Learning Classifiers ◽  
Human Eye ◽  
Deep Convolution Neural Network ◽  
Similar Appearance

It is easy for a human eye to distinguish the images of similar appearance but classifying the images like that of cancer affected skin  requires more expertise. And as the skin cancer cases are increasing globally, it requires more number of human experts. To overcome this problem, many people are working on constructing machine learning classifiers which can detect skin cancer automatically by    classifying skin images. This paper concentrates on developing an approach for predicting skin cancer by classifying images using deep convolution neural network. The proposed work is tested on standard cancer dataset and obtained more than 85% accuracy. 

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