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.

scholarly journals Detection & Classification of Lung Cancer at an Early Stage by Applying Feature Extraction-Optimization and Neural Network on Hybrid Structure

2019 ◽  
Vol 9 (2) ◽  
pp. 3737-3745
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Feature Extraction ◽  
Hybrid Model ◽  
Error Rate ◽  
Execution Time ◽  
Early Stage ◽  
Support Vector ◽  
Minimum Error

As of now the detection and classification of lung cancer disease is one of the most tedious tasks in the field of medical area. In the diversified sector of medical industry usage of technology plays a very important role. Detection and diagnosis of the lung cancer at an early stage with more accuracy is the most challenging task. So, in this research article 400 set of images has been used for this experiment. Best feature extraction technique and best feature optimization technique has been analyzed on the basis of parameter minimum execution time with minimum error rate. Then finest selection of features leads to an optimal classification. In this context, one of the best classification algorithm the support vector machine has been proposed in this hybrid model for the binary classification. Further Feed forward back propagation neural network has been implemented with SVM. This proposed hybrid model reduces the complexity of the system on the basis of minimum execution time that is 1.94 sec. with minimum error rate 29.25. Further better classification accuracy 99.6507% has been achieved by using this unique hybrid model

scholarly journals Three Stream Network Model for Lung Cancer Classification in the CT Images

2021 ◽  
Vol 11 (1) ◽  
pp. 251-261
Author(s):  
T. Arumuga Maria Devi ◽  
V. I. Mebin Jose
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Deep Neural Network ◽  
Early Stage ◽  
Low Frequency ◽  
Ct Images ◽  
Input Image ◽  
Support Vector ◽  
Human Beings ◽  
Stream Network

Abstract Lung cancer is considered to be one of the deadly diseases that threaten the survival of human beings. It is a challenging task to identify lung cancer in its early stage from the medical images because of the ambiguity in the lung regions. This paper proposes a new architecture to detect lung cancer obtained from the CT images. The proposed architecture has a three-stream network to extract the manual and automated features from the images. Among these three streams, automated feature extraction as well as the classification is done using residual deep neural network and custom deep neural network. Whereas the manual features are the handcrafted features obtained using high and low-frequency sub-bands in the frequency domain that are classified using a Support Vector Machine Classifier. This makes the architecture robust enough to capture all the important features required to classify lung cancer from the input image. Hence, there is no chance of missing feature information. Finally, all the obtained prediction scores are combined by weighted based fusion. The experimental results show 98.2% classification accuracy which is relatively higher in comparison to other existing methods.

scholarly journals NEURAL NETWORK ANALYSIS FOR TUMOR INVESTIGATION AND CANCER PREDICTION

2019 ◽  
Vol 2019 (02) ◽  
pp. 89-98
Author(s):  
Vijayakumar T
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Early Stage ◽  
Original Data ◽  
Data Set ◽  
Cancer Prediction ◽  
The Neural Network ◽  
Feed Forward Neural Networks ◽  
The Neural Networks ◽  
Human Nervous System

Predicting the category of tumors and the types of the cancer in its early stage remains as a very essential process to identify depth of the disease and treatment available for it. The neural network that functions similar to the human nervous system is widely utilized in the tumor investigation and the cancer prediction. The paper presents the analysis of the performance of the neural networks such as the, FNN (Feed Forward Neural Networks), RNN (Recurrent Neural Networks) and the CNN (Convolutional Neural Network) investigating the tumors and predicting the cancer. The results obtained by evaluating the neural networks on the breast cancer Wisconsin original data set shows that the CNN provides 43 % better prediction than the FNN and 25% better prediction than the RNN.

scholarly journals Multimodal Few-Shot Learning for Gait Recognition

2020 ◽  
Vol 10 (21) ◽  
pp. 7619
Author(s):  
Jucheol Moon ◽  
Nhat Anh Le ◽  
Nelson Hebert Minaya ◽  
Sang-Il Choi
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Gait Recognition ◽  
Gait Pattern ◽  
Recognition Problem ◽  
Human Gait ◽  
Training Dataset ◽  
Support Vector ◽  
Open Set ◽  
Latent Space

A person’s gait is a behavioral trait that is uniquely associated with each individual and can be used to recognize the person. As information about the human gait can be captured by wearable devices, a few studies have led to the proposal of methods to process gait information for identification purposes. Despite recent advances in gait recognition, an open set gait recognition problem presents challenges to current approaches. To address the open set gait recognition problem, a system should be able to deal with unseen subjects who have not included in the training dataset. In this paper, we propose a system that learns a mapping from a multimodal time series collected using insole to a latent (embedding vector) space to address the open set gait recognition problem. The distance between two embedding vectors in the latent space corresponds to the similarity between two multimodal time series. Using the characteristics of the human gait pattern, multimodal time series are sliced into unit steps. The system maps unit steps to embedding vectors using an ensemble consisting of a convolutional neural network and a recurrent neural network. To recognize each individual, the system learns a decision function using a one-class support vector machine from a few embedding vectors of the person in the latent space, then the system determines whether an unknown unit step is recognized as belonging to a known individual. Our experiments demonstrate that the proposed framework recognizes individuals with high accuracy regardless they have been registered or not. If we could have an environment in which all people would be wearing the insole, the framework would be used for user verification widely.

scholarly journals Lung Cancer Detection Using Marker Controlled Watershed with SVM

2018 ◽  
Vol 5 (1) ◽  
pp. 24-30
Author(s):  
Fatema Tuj Johora ◽  
Mehdi Hassan Jony ◽  
Md Shakhawat Hossain ◽  
Humayun Kabir Rana
Keyword(s):  
Lung Cancer ◽  
Cancer Cells ◽  
Cancer Detection ◽  
Early Stage ◽  
Support Vector ◽  
Lung Cells ◽  
Science And Engineering ◽  
Efficient Performance ◽  
Occurrence Matrix ◽  
Lung Cancer Detection

Lung cancer is one of the most dangerous diseases and prediction of it, is the most challenging problem nowadays. Most of the cancer cells are overlapped with each other. It is hard to detect the cells but also essential to identify the presence of cancer cells in the early stage. Early detection of lung cancer may reduce the death rate. In this study, we used the Grey Level Co-occurrence Matrix (GLCM) to extract the feature of cancer affected lung image and then Support Vector Machine (SVM) has been used to detect normal and abnormal lung cells after implementing the features. Our experimental evaluation using MATLAB demonstrates the efficient performance of the proposed system and in the result. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 5(1), Dec 2018 P 24-30

scholarly journals Evaluating structural safety of trusses using Machine Learning

2021 ◽  
Vol 15 (58) ◽  
pp. 308-318
Author(s):  
Tran-Hieu Nguyen ◽  
Anh-Tuan Vu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Roc Curve ◽  
Deep Neural Network ◽  
Structural Safety ◽  
Training Dataset ◽  
Support Vector ◽  
Machine Model ◽  
Adaptive Boosting

In this paper, a machine learning-based framework is developed to quickly evaluate the structural safety of trusses. Three numerical examples of a 10-bar truss, a 25-bar truss, and a 47-bar truss are used to illustrate the proposed framework. Firstly, several truss cases with different cross-sectional areas are generated by employing the Latin Hypercube Sampling method. Stresses inside truss members as well as displacements of nodes are determined through finite element analyses and obtained values are compared with design constraints. According to the constraint verification, the safety state is assigned as safe or unsafe. Members’ sectional areas and the safety state are stored as the inputs and outputs of the training dataset, respectively. Three popular machine learning classifiers including Support Vector Machine, Deep Neural Network, and Adaptive Boosting are used for evaluating the safety of structures. The comparison is conducted based on two metrics: the accuracy and the area under the ROC curve. For the two first examples, three classifiers get more than 90% of accuracy. For the 47-bar truss, the accuracies of the Support Vector Machine model and the Deep Neural Network model are lower than 70% but the Adaptive Boosting model still retains the high accuracy of approximately 98%. In terms of the area under the ROC curve, the comparative results are similar. Overall, the Adaptive Boosting model outperforms the remaining models. In addition, an investigation is carried out to show the influence of the parameters on the performance of the Adaptive Boosting model.

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 Sistem Pendukung Keputusan Klinis dengan Memanfaatkan Jaringan Syaraf Tiruan Untuk Mendeteksi Stadium Penderita Kanker Paru-Paru Jenis Karsinoma Bukan Sel Kecil

2011 ◽  
Vol 5 (2) ◽  
Author(s):  
Rio Kurniawan ◽  
Sri Hartati
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Learning Process ◽  
Clinical Decision Support System ◽  
Early Stage ◽  
Clinical Decision

Abstract-- Lung cancer is leading cause of death in the cancer group. In general, lung cancer has some symptoms, but at an early stage, symptoms are not perceived by the patient. As a result, when patients go to hospital, lung cancer has been diagnosed in middle or high stage. For early detection of lung cancer, necessary a decision support system based on computerized technology that can be utilized by doctor needed to detection lung cancer. The clinical decision support system will help to determine specific medical treatment. The clinical decision support system capable to know data input and produce output result by learning process. The learning process is  part of process in artificial neural network (ANN). Many methods used in ANN as Backpropagation (BP)learning algorithm. BP used to produce output result in decision support system. Keywords-- lung cancer, stage, clinical decision support systems, neural network, multilayer perceptron, backpropagation algorithm

scholarly journals Laboratory Testing Implications of Risk-Stratification and Management of COVID-19 Patients

2021 ◽  
Author(s):  
Caidong Liu ◽  
Ziyu Wang ◽  
Wei Wu ◽  
Changgang Xiang ◽  
Lingxiang Wu ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Stratification ◽  
Early Stage ◽  
Case Series ◽  
Low Risk ◽  
Training Dataset ◽  
Support Vector ◽  
Machine Model ◽  
Testing Dataset ◽  
Probability Of Death

Abstract Objectives: To classify COVID-19 patients into low-risk and high-risk at admission by laboratory indicators.Design, Setting, and Patients: This is a case series of patients from a China healthcare system in Wuhan. In this retrospective cohort, 3563 patients confirmed COVID-19 pneumonia, including 548 patients in the training dataset, and 3015 patients in the testing dataset.Interventions: NoneMeasurements and Main Results:We first identified the significant laboratory indicators related to the severity of COVID-19 in the training dataset. Neutrophils percentage, lymphocytes percentage, creatinine, and blood urea nitrogen with AUC greater than 0.7 were included in the model. These indicators were further used to build a support vector machine model to classify patients into low-risk and high-risk at admission. Results showed that this model could stratify the patients in the testing dataset effectively (AUC=0.89). Moreover, laboratory indicators detected in the first week after admission were able to estimate the probability of death (AUC=0.95). Besides, we could diagnose COVID-19 and differentiated it from other kinds of viral pneumonia based on laboratory indicators (accuracy=0.97).Conclusions:Our risk-stratification model based on laboratory indicators could help to diagnose, monitor, and predict severity at an early stage of COVID-19.

scholarly journals Diagnosis of Alzheimer’s Disease Severity with fMRI Images Using Robust Multitask Feature Extraction Method and Convolutional Neural Network (CNN)

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Morteza Amini ◽  
MirMohsen Pedram ◽  
AliReza Moradi ◽  
Mahshad Ouchani
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Early Stage ◽  
Support Vector ◽  
Feature Extraction Method

The automatic diagnosis of Alzheimer’s disease plays an important role in human health, especially in its early stage. Because it is a neurodegenerative condition, Alzheimer’s disease seems to have a long incubation period. Therefore, it is essential to analyze Alzheimer’s symptoms at different stages. In this paper, the classification is done with several methods of machine learning consisting of K -nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), linear discrimination analysis (LDA), and random forest (RF). Moreover, novel convolutional neural network (CNN) architecture is presented to diagnose Alzheimer’s severity. The relationship between Alzheimer’s patients’ functional magnetic resonance imaging (fMRI) images and their scores on the MMSE is investigated to achieve the aim. The feature extraction is performed based on the robust multitask feature learning algorithm. The severity is also calculated based on the Mini-Mental State Examination score, including low, mild, moderate, and severe categories. Results show that the accuracy of the KNN, SVM, DT, LDA, RF, and presented CNN method is 77.5%, 85.8%, 91.7%, 79.5%, 85.1%, and 96.7%, respectively. Moreover, for the presented CNN architecture, the sensitivity of low, mild, moderate, and severe status of Alzheimer patients is 98.1%, 95.2%,89.0%, and 87.5%, respectively. Based on the findings, the presented CNN architecture classifier outperforms other methods and can diagnose the severity and stages of Alzheimer’s disease with maximum accuracy.

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