A hybrid feature extraction approach for the detection of melanoma using neural network

In spite of the gargantuan number of patients affected by melanoma every year, its detection at an early stage is still a challenging task. This paper illustrates a method which involves the combination of the existing ABCD (Involving symmetry, border, color, and diameter detection) rule and grey level co-occurrence matrix (GLCM) along with Local Binary Pattern (LBP) for identification of malignant melanoma skin lesion with greater accuracy. Several steps, such as image acquisition technique, pre-processing (RGB to HSV) techniques and segmentation processes are undertaken for the skin feature selection criteria to successfully determine the skin lesion's characteristic properties for classification. Texture features such as contrast, entropy, energy and homogeneity of the affected region is obtained using LBP and GLCM for discriminatory purposes of the two cases (melanoma and non-melanoma). Finally, the back propagation neural network (BPN) is used as the classifier to determine whether the dermoscopic image is benign or malignant.

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The combination of gray level co-occurrence matrix and back propagation neural network for classifying stairs descent and floor

ICT Express ◽

10.1016/j.icte.2021.05.010 ◽

2021 ◽

Author(s):

Fitri Utaminingrum ◽

Syam Julio A. Sarosa ◽

Corina Karim ◽

Femiana Gapsari ◽

Randy Cahya Wihandika

Keyword(s):

Neural Network ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Gray Level ◽

Occurrence Matrix

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Diagnostic Prediction Models for Cardiovascular Disease Risk using Data Mining Techniques

ECTI Transactions on Computer and Information Technology (ECTI-CIT) ◽

10.37936/ecti-cit.2020142.199897 ◽

2020 ◽

Vol 14 (2) ◽

pp. 113-121

Author(s):

Nongyao Nai-arun ◽

Rungruttikarn Moungmai

Keyword(s):

Neural Network ◽

Cardiovascular Disease ◽

Logistic Regression ◽

Prediction Models ◽

Disease Risk ◽

Cardiovascular Disease Risk ◽

Back Propagation ◽

Risk Groups ◽

Back Propagation Neural Network ◽

Number Of Patients

Cardiovascular disease is the top national health problem that leads to a big number of deaths in Thailand. There is still a growing number of patients with the disease. Proactive measures of disease prevention and disease control are searching for risk groups. Therefore, people who are at risk can diagnose and manage themselves to reduce risk factors and adjust their behavior accordingly. For this reason, the idea of diagnostic prediction models for Cardiovascular was conducted. The data of patients from 126 health promoting hospitals and 12 hospitals in Saraburi Province were collected. Then, the analysis was done to establish 6 models namely logistic regression, random forest, back-propagation neural network, decision tree, naïve bayes and K-nearest neighbors. Moreover, 10-fold cross validation was applied into the process of each model. The results revealed that the logistic regression model achieved the highest accuracy rate, 99.940%, followed by the back-propagation neural network model, 98.506%. The best model should be developed as a web application to search for new patients or risk groups. It will help to prevent and control the disease quickly and also to reduce mortality.

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LBP and GLCM Based Image Forgery Recognition

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.6.20478 ◽

2018 ◽

Vol 7 (4.6) ◽

pp. 217

Author(s):

D. Vaishnavi ◽

T. S. Subashini ◽

G. N. Balaji ◽

D. Mahalakshmi

Keyword(s):

Neural Network ◽

Local Binary Pattern ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Support Vector ◽

Gray Level ◽

Image Splicing ◽

Naked Eye ◽

Occurrence Matrix

The forgery of digital images became very easy and it’s very difficult to ascertain the authenticity of such images by naked eye. Among the various kinds of image forgeries, image splicing is a frequent and widely used technique. Even though various methods are available to detect image splicing forgery, authors have attempted to provide a novel hybrid method which can yield greater accuracy, sensitivity and specificity. In this method, gray level co-occurrence matrix (GLCM) features are extracted using local binary pattern (LBP) operator on the image and the detection of the splicing forged images among the authentic images is done using the popular pattern recognition algorithms such as combined k-NN (Comb-KNN), back propagation neural network (BPNN) and support vector machine (SVM). The recorded results are also compared with the existing results of the previous studies to ascertain the quality of the results.

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Hidden Neuron Analysis for Detection Cataract Disease Based on Gray Level Co-occurrence Matrix and Back Propagation Neural Network

10.1109/iciss53185.2021.9533263 ◽

2021 ◽

Author(s):

Tiara Sri Mulati ◽

Fitri Utaminingrum

Keyword(s):

Neural Network ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Gray Level ◽

Occurrence Matrix

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Brain Tumor Detection System Based on Sending Email Using Gray Level Co-Occurrence Matrix and Back-Propagation Neural Network

10.1145/3479645.3479665 ◽

2021 ◽

Author(s):

Asep Ranta Munajat ◽

Fitri Utaminingrum

Keyword(s):

Neural Network ◽

Brain Tumor ◽

Detection System ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Tumor Detection ◽

Gray Level ◽

Occurrence Matrix

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Economic benefit of shale gas exploitation based on back propagation neural network

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189279 ◽

2020 ◽

Vol 39 (6) ◽

pp. 8823-8830

Author(s):

Jiafeng Li ◽

Hui Hu ◽

Xiang Li ◽

Qian Jin ◽

Tianhao Huang

Keyword(s):

Neural Network ◽

Shale Gas ◽

Bp Neural Network ◽

Large Scale ◽

Linear Prediction ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Economic Benefits ◽

Gas Well ◽

Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

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An Effective Customer Churn Prediction Model using Adaptive Gain with Back Propagation Neural Network in Cloud Computing Environment

Journal of Research on the Lepidoptera ◽

10.36872/lepi/v51i1/301034 ◽

2020 ◽

Vol 51 (1) ◽

pp. 386-399

Author(s):

JEYAKARTHIC M

Keyword(s):

Neural Network ◽

Cloud Computing ◽

Prediction Model ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Churn Prediction ◽

Computing Environment ◽

Cloud Computing Environment ◽

Customer Churn ◽

Customer Churn Prediction

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An Anatomy of a Hybrid Color Descriptor with a Neural Network Model to Enhance the Retrieval Accuracy of an Image Retrieval System

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813666191122113801 ◽

2019 ◽

Vol 13 ◽

Author(s):

Shikha Bhardwaj ◽

Gitanjali Pandove ◽

Pawan Kumar Dahiya

Keyword(s):

Neural Network ◽

Deep Learning ◽

Image Retrieval ◽

Hybrid System ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Retrieval Accuracy ◽

Color Descriptor ◽

Benchmark Datasets ◽

Color Moment

Background: In order to retrieve a particular image from vast repository of images, an efficient system is required and such an eminent system is well-known by the name Content-based image retrieval (CBIR) system. Color is indeed an important attribute of an image and the proposed system consist of a hybrid color descriptor which is used for color feature extraction. Deep learning, has gained a prominent importance in the current era. So, the performance of this fusion based color descriptor is also analyzed in the presence of Deep learning classifiers. Method: This paper describes a comparative experimental analysis on various color descriptors and the best two are chosen to form an efficient color based hybrid system denoted as combined color moment-color autocorrelogram (Co-CMCAC). Then, to increase the retrieval accuracy of the hybrid system, a Cascade forward back propagation neural network (CFBPNN) is used. The classification accuracy obtained by using CFBPNN is also compared to Patternnet neural network. Results: The results of the hybrid color descriptor depict that the proposed system has superior results of the order of 95.4%, 88.2%, 84.4% and 96.05% on Corel-1K, Corel-5K, Corel-10K and Oxford flower benchmark datasets respectively as compared to many state-of-the-art related techniques. Conclusion: This paper depict an experimental and analytical analysis on different color feature descriptors namely, Color moment (CM), Color auto-correlogram (CAC), Color histogram (CH), Color coherence vector (CCV) and Dominant color descriptor (DCD). The proposed hybrid color descriptor (Co-CMCAC) is utilized for the withdrawal of color features with Cascade forward back propagation neural network (CFBPNN) is used as a classifier on four benchmark datasets namely Corel-1K, Corel-5K and Corel-10K and Oxford flower.

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