scholarly journals Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network

2018 ◽  
Vol 5 (1) ◽  
pp. 23-30 ◽  
Author(s):  
N. Varuna Shree ◽  
T. N. R. Kumar
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Brain Tumor ◽  
Probabilistic Neural Network ◽  
Tumor Mri

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.

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 Differential Deep Convolutional Neural Network Model for Brain Tumor Classification

2021 ◽  
Vol 11 (3) ◽  
pp. 352
Author(s):  
Isselmou Abd El Kader ◽  
Guizhi Xu ◽  
Zhang Shuai ◽  
Sani Saminu ◽  
Imran Javaid ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Medical Image ◽  
Resonance Imaging ◽  
Feature Maps ◽  
Deep Cnn

The classification of brain tumors is a difficult task in the field of medical image analysis. Improving algorithms and machine learning technology helps radiologists to easily diagnose the tumor without surgical intervention. In recent years, deep learning techniques have made excellent progress in the field of medical image processing and analysis. However, there are many difficulties in classifying brain tumors using magnetic resonance imaging; first, the difficulty of brain structure and the intertwining of tissues in it; and secondly, the difficulty of classifying brain tumors due to the high density nature of the brain. We propose a differential deep convolutional neural network model (differential deep-CNN) to classify different types of brain tumor, including abnormal and normal magnetic resonance (MR) images. Using differential operators in the differential deep-CNN architecture, we derived the additional differential feature maps in the original CNN feature maps. The derivation process led to an improvement in the performance of the proposed approach in accordance with the results of the evaluation parameters used. The advantage of the differential deep-CNN model is an analysis of a pixel directional pattern of images using contrast calculations and its high ability to classify a large database of images with high accuracy and without technical problems. Therefore, the proposed approach gives an excellent overall performance. To test and train the performance of this model, we used a dataset consisting of 25,000 brain magnetic resonance imaging (MRI) images, which includes abnormal and normal images. The experimental results showed that the proposed model achieved an accuracy of 99.25%. This study demonstrates that the proposed differential deep-CNN model can be used to facilitate the automatic classification of brain tumors.

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