scholarly journals Classification of malignant and Benign Lung Using Probabilistic Neural Network

2019 ◽  
pp. 307-311
Author(s):  
Asmitha Shree R ◽  
Sajitha M ◽  
Subha S
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Lung Cancer ◽  
Feature Extraction ◽  
Probabilistic Neural Network ◽  
Principal Component ◽  
Classification Performance ◽  
Cancer Types ◽  
Extraction Stage

Lung Cancer is considered as one of the deadliest diseases among other lung disorders and cancer types and is the leading cause of cancer deaths worldwide. Lung cancer is a curable disease if detected in its early stages that makes up 13% of all cancer diagnoses and 27% of all cancer deaths. The objective of this paper is mainly focused on categorizing the patients Computed Tomography (CT) lung images as normal or abnormal. The images are subjected to segmentation to focus on detecting the cancerous region to classify. Effective feature selection and feature extraction is made by applying Watershed Transform and Principal Component Analysis. The emphasis is on the feature extraction stage to yield a better classification performance. The classification of CT images as benign or malignant is done using Machine Learning based Neural Network.

scholarly journals A Computer-Aided Pipeline for Automatic Lung Cancer Classification on Computed Tomography Scans

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Emre Dandıl
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Probabilistic Neural Network ◽  
Principal Component ◽  
Treatment Method ◽  
Feature Reduction ◽  
Pipeline System ◽  
Self Organizing Maps ◽  
Computer Aided ◽  
Combined Features

Lung cancer is one of the most common cancer types. For the survival of the patient, early detection of lung cancer with the best treatment method is crucial. In this study, we propose a novel computer-aided pipeline on computed tomography (CT) scans for early diagnosis of lung cancer thanks to the classification of benign and malignant nodules. The proposed pipeline is composed of four stages. In preprocessing steps, CT images are enhanced, and lung volumes are extracted from the image with the help of a novel method called lung volume extraction method (LUVEM). The significance of the proposed pipeline is using LUVEM for extracting lung region. In nodule detection stage, candidate nodules are determined according to the circular Hough transform- (CHT-) based method. Then, lung nodules are segmented with self-organizing maps (SOM). In feature computation stage, intensity, shape, texture, energy, and combined features are used for feature extraction, and principal component analysis (PCA) is used for feature reduction step. In the final stage, probabilistic neural network (PNN) classifies benign and malign nodules. According to the experiments performed on our dataset, the proposed pipeline system can classify benign and malign nodules with 95.91% accuracy, 97.42% sensitivity, and 94.24% specificity. Even in cases of small-sized nodules (3–10 mm), the proposed system can determine the nodule type with 94.68% accuracy.

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 A novel hybrid approach of SVM combined with NLP and probabilistic neural network for email phishing

2020 ◽  
Vol 10 (1) ◽  
pp. 486 ◽  
Author(s):  
Abhishek Kumar ◽  
Jyotir Moy Chatterjee ◽  
Vicente García Díaz
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Probabilistic Neural Network ◽  
Hybrid Approach ◽  
Cyber Attacks ◽  
Hybrid Analysis ◽  
Phishing Attacks ◽  
Phishing Detection ◽  
Hybrid Methodology

Phishing attacks are one of the slanting cyber-attacks that apply socially engineered messages that are imparted to individuals from expert hackers going for tricking clients to uncover their delicate data, the most mainstream correspondence channel to those messages is through clients' emails. Phishing has turned into a generous danger for web clients and a noteworthy reason for money related misfortunes. Therefore, different arrangements have been created to handle this issue. Deceitful emails, also called phishing emails, utilize a scope of impact strategies to convince people to react, for example, promising a fiscal reward or summoning a feeling of criticalness. Regardless of far reaching alerts and intends to instruct clients to distinguish phishing sends, these are as yet a pervasive practice and a worthwhile business. The creators accept that influence, as a style of human correspondence intended to impact others, has a focal job in fruitful advanced tricks. Cyber criminals have ceaselessly propelling their techniques for assault. The current strategies to recognize the presence of such malevolent projects and to keep them from executing are static, dynamic and hybrid analysis. In this work we are proposing a hybrid methodology for phishing detection incorporating feature extraction and classification of the mails using SVM. At last, alongside the chose features, the PNN characterizes the spam mails from the genuine mails with more exactness and accuracy.

Classification and Feature Extraction

2018 ◽  
pp. 150-168
Keyword(s):  
Neural Network ◽  
Myocardial Infarction ◽  
Feature Extraction ◽  
Classification Performance ◽  
Feedforward Neural Network ◽  
Anterior Myocardial Infarction ◽  
Ecg Signals ◽  
Gas Molecule ◽  
Gas Molecules

In this chapter, the proposed optimization algorithm, kinetic gas molecule optimization (KGMO), that is based on swarm behaviour of gas molecules is applied to train a feedforward neural network for classification of ECG signals. Five types of ECG signals are used in this work including normal, supraventricular, brunch bundle block, anterior myocardial infarction (Anterior MI), and interior myocardial infarction (Interior MI). The classification performance of the proposed KGMO neural network (KGMONN) was evaluated on the Physiobank database and compared against conventional algorithms.

scholarly journals Detection and Classification of Recessive Weakness in Superbuck Converter Based on WPD-PCA and Probabilistic Neural Network

Electronics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 290 ◽  
Author(s):  
Chenhao Wu ◽  
Jiguang Yue ◽  
Li Wang ◽  
Feng Lyu
Keyword(s):  
Neural Network ◽  
Classification Accuracy ◽  
Wavelet Decomposition ◽  
Probabilistic Neural Network ◽  
Wavelet Packet ◽  
Principal Component ◽  
Excellent Performance ◽  
Feature Vectors ◽  
Parseval’S Theorem

This paper proposes a detection and classification method of recessive weakness in Superbuck converter through wavelet packet decomposition (WPD) and principal component analysis (PCA) combined with probabilistic neural network (PNN). The Superbuck converter presents excellent performance in many applications and is also faced with today’s demands, such as higher reliability and steadier operation. In this paper, the detection and classification issue to recessive weakness is settled. Firstly, the performance of recessive weakness both in the time and frequency domain are demonstrated to clearly show the actual deterioration of the circuit system. The WPD and Parseval’s theorem are utilized in this paper to feature the extraction of recessive weakness. The energy discrepancy of the fault signals at different wavelet decomposition levels are then chosen as the feature vectors. PCA is also employed to the dimensionality reduction of feature vectors. Then, a probabilistic neural network is applied to automatically detect and classify the recessive weakness from different components on the basis of the extracted features. Finally, the classification accuracy of the proposed classification algorithm is verified and tested with experiments, which present satisfying classification accuracy.

scholarly journals Probabilistic Neural Network based Approach for Handwritten Character Recognition

2010 ◽  
pp. 105-109
Author(s):  
V.N. Manjunath Aradhya ◽  
S. K. Niranjan ◽  
G. Hemantha Kumar
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Character Recognition ◽  
Probabilistic Neural Network ◽  
Principal Component ◽  
Recognition System ◽  
Indian Language ◽  
South Indian ◽  
Extraction Scheme ◽  
Standard Techniques

In this paper, recognition system for totally unconstrained handwritten characters for south Indian language of Kannada is proposed. The proposed feature extraction technique is based on Fourier Transform and well known Principal Component Analysis (PCA). The system trains the appropriate frequency band images followed by PCA feature extraction scheme. For subsequent classification technique, Probabilistic Neural Network (PNN) is used. The proposed system is tested on large database containing Kannada characters and also tested on standard COIL-20 object database and the results were found to be better compared to standard techniques.

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