scholarly journals Early Lung Cancer Prediction Using Neural Network with Cross-Validation

2020 ◽  
Author(s):  
Samir Bandyopadhyay ◽  
Shawni Dutta
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Cross Validation ◽  
Gradient Boosting ◽  
Predictive Tool ◽  
Cancer Disease ◽  
Counter Measures ◽  
Cancer Prediction ◽  
Validation Technique ◽  
Automated Tool

Lung cancer is known as lung carcinoma. It is a disease which is malignant tumor leading to the uncontrolled cell growth in the lung tissue. Lung Cancer disease is one of the most prominent cause of death in all over world. Early detection of this disease can assist medical care unit as well as physicians to provide counter measures to the patients. The objective of this paper is to approach an automated tool that takes influential causes of lung cancer as input and detect patients with higher probabilities of being affected by this disease. A neural network classifier accompanied by cross-validation technique is proposed in this paper as a predictive tool. Later, this proposed method is compared with another baseline classifier Gradient Boosting Classifier in order to justify the prediction performance.

scholarly journals Early Lung Cancer Prediction Using Neural Network with Cross-validation

2020 ◽  
pp. 15-22
Author(s):  
Shawni Dutta ◽  
Samir Kumar Bandyopadhyay
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Cross Validation ◽  
Small Cell ◽  
Disease Classification ◽  
Classification Model ◽  
Gradient Boosting ◽  
Small Cell Lung ◽  
Predictive Tool ◽  
Lung Carcinomas

Lung cancer is known as lung carcinoma. It is a disease which is malignant tumor leading to the uncontrolled cell growth in the lung tissue. Lung cancer is caused generally by smoking and the use of tobacco products. It is classified into two broad Small-cell lung Carcinomas and non-Small cell lung carcinomas. Lung cancer treatments include surgery, radiation therapy, chemotherapy, and targeted therapy. Lung Cancer disease is one of the most prominent cause of death in all over world. Early detection of this disease can assist medical care unit as well as physicians to provide counter measures to the patients. The objective of this paper is to approach an automated tool that takes influential causes of lung cancer as input and detect patients with higher probabilities of being affected by this disease. A neural network classifier accompanied by k-fold cross-validation technique is proposed in this paper as a predictive tool. Later, this proposed method is compared with another baseline classifier Gradient Boosting Classifier in order to justify the prediction performance. Experimental results conclude that analyzing interfering causes of lung cancer can effectively accomplish disease classification model with an accuracy of 95%.

scholarly journals Modeling Baseline Energy Using Artificial Neural Network – A Small Dataset Approach

2018 ◽  
Vol 12 (2) ◽  
pp. 662
Author(s):  
Wan Nazirah Wan Md Adnan ◽  
Nofri Yenita Dahlan ◽  
Ismail Musirin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cross Validation ◽  
Model Development ◽  
Energy Model ◽  
Ann Model ◽  
Data Set ◽  
Validation Technique ◽  
Artificial Neural ◽  
Small Dataset

In this work, baseline energy model development using Artificial Neural Network (ANN) with resampling techniques; Cross Validation (CV) and Bootstrap (BS) are presented. Resampling techniques are used to examine the ability of the ANN model to deal with a small dataset. Working days, class days and Cooling Degree Days (CDD) are used as ANN input meanwhile the ANN output is monthly electricity consumption. The coefficient of correlation (R) is used as performance function to evaluate the model accuracy. For this analysis, R is calculated for the entire data set (R_all) and separately for training set (R_train), validation set (R_valid) dan testing set (R_test). The closer R to 1, the higher similarities between targeted and predicted output. The total of two different models with several number of neurons are developed and compared. It can be concluded that all models are capable to train the network. Artificial Neural Network with Bootstrap Cross Validation technique (ANN-BSCV) outperforms Artificial Neural Network with Cross Validation technique (ANN-CV).  The 3-6-1 ANN-BSCV, with R_train = 0.95668, R_valid = 0.97553, R_test = 0.85726 and R_all = 0.94079 is selected as the baseline energy model to predict energy consumption for Option C IPMVP.

scholarly journals Lung Cancer Prediction Using Neural Network Ensemble with Histogram of Oriented Gradient Genomic Features

2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
Emmanuel Adetiba ◽  
Oludayo O. Olugbara
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Early Stage ◽  
Molecular Therapy ◽  
Training Dataset ◽  
Experimental Comparison ◽  
Support Vector ◽  
Histogram Of Oriented Gradient ◽  
Genomic Features ◽  
Cancer Prediction

This paper reports an experimental comparison of artificial neural network (ANN) and support vector machine (SVM) ensembles and their “nonensemble” variants for lung cancer prediction. These machine learning classifiers were trained to predict lung cancer using samples of patient nucleotides with mutations in the epidermal growth factor receptor, Kirsten rat sarcoma viral oncogene, and tumor suppressor p53 genomes collected as biomarkers from the IGDB.NSCLC corpus. The Voss DNA encoding was used to map the nucleotide sequences of mutated and normal genomes to obtain the equivalent numerical genomic sequences for training the selected classifiers. The histogram of oriented gradient (HOG) and local binary pattern (LBP) state-of-the-art feature extraction schemes were applied to extract representative genomic features from the encoded sequences of nucleotides. The ANN ensemble and HOG best fit the training dataset of this study with an accuracy of 95.90% and mean square error of 0.0159. The result of the ANN ensemble and HOG genomic features is promising for automated screening and early detection of lung cancer. This will hopefully assist pathologists in administering targeted molecular therapy and offering counsel to early stage lung cancer patients and persons in at risk populations.

Boosted neural network ensemble classification for lung cancer disease diagnosis

2019 ◽  
Vol 80 ◽  
pp. 579-591 ◽  
Author(s):  
Jafar A. ALzubi ◽  
Balasubramaniyan Bharathikannan ◽  
Sudeep Tanwar ◽  
Ramachandran Manikandan ◽  
Ashish Khanna ◽  
...  
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Disease Diagnosis ◽  
Ensemble Classification ◽  
Neural Network Ensemble ◽  
Cancer Disease

scholarly journals In Silico Model for Lung Cancer Prediction Based on TP53 mutations Using Neural Network

2018 ◽  
Vol 00 (1) ◽  
pp. 196-201
Author(s):  
Zahraa Naser Shahweli ◽  
◽  
Ban Nadeem Dhannoon ◽  
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
In Silico ◽  
Tp53 Mutations ◽  
Cancer Prediction ◽  
In Silico Model

scholarly journals Copy number variation in plasma as a tool for lung cancer prediction using Extreme Gradient Boosting (XGBoost) classifier

2019 ◽  
Vol 11 (1) ◽  
pp. 95-102 ◽  
Author(s):  
Daping Yu ◽  
Zhidong Liu ◽  
Chongyu Su ◽  
Yi Han ◽  
XinChun Duan ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Copy Number Variation ◽  
Copy Number ◽  
Gradient Boosting ◽  
Cancer Prediction ◽  
Extreme Gradient Boosting ◽  
Number Variation

Lung cancer prediction using higher-order recurrent neural network based on glowworm swarm optimization

2018 ◽  
Vol 32 (9) ◽  
pp. 4373-4386 ◽  
Author(s):  
Ramani Selvanambi ◽  
Jaisankar Natarajan ◽  
Marimuthu Karuppiah ◽  
SK Hafizul Islam ◽  
Mohammad Mehedi Hassan ◽  
...  
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Recurrent Neural Network ◽  
Higher Order ◽  
Swarm Optimization ◽  
Cancer Prediction ◽  
Glowworm Swarm Optimization

scholarly journals Diagnosis of Lung Cancer Disease Based on Back-Propagation Artificial Neural Network Algorithm

2020 ◽  
Vol 38 (3B) ◽  
pp. 184-196
Author(s):  
Hanan A. R. Akkar ◽  
Suhad Q. Hadad
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Artificial Neural Network ◽  
Early Stage ◽  
Back Propagation ◽  
Graphic User Interface ◽  
Cancer Disease ◽  
Intelligent Technique ◽  
Artificial Neural ◽  
Diagnosis Of Lung Cancer

Early stage detection of lung cancer is important for successful controlling of the diseases, also to offer additional chance to the patients in order to survive. So , algorithms that are related with computer vision and Image processing are extremely important for early medical diagnosis of lung cancer. In current work () computed tomography scan images were collected from several patients Classification was done using Back Propagation Artificial Neural Network ( ).It is considered as a powerful artificially intelligent technique with training rule for optimization to update the weights of the overall connections in order to determine the abnormal image. Several pre-processing operations and morphologic techniques were introduced to improve the condition of the image and make it suitable for detection cancer.Histogram and () Gray Level Co-occurrence Matrix were applied toget best features extraction analysis from lung image.Three types of activation functions(trainlm ,trainbr ,traingd) were used which gives a significant accuracy for detecting cancer in  scan lung image related to the suggested algorithm. Best results were obtained with accuracy rate 95.9 % in trainlm activation function.. Graphic User Interface ( ) was displaying to show the final diagnosis  for lung.

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