Brain Tumor Classification using Principal Component Analysis and Probabilistic Neural Network

Gaikwad, Sonali; Joshi, Madhuri S.

doi:10.5120/21205-3885

Cited by 32 publications

(17 citation statements)

References 7 publications

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“…EM+PCNN [50] FFT(fast fourier transform) + EM -GMM [51] FCM + LVQ [52] DWT + FCM [53] AC (active contour) + SVM [54] DWT + SOM [55] GA + SVM [56,57] Level set + ANN [58] GA + ANN [59] GR (generalized rough) + FCM [60] DWT + GA + SVM [61] SOM + LVQ [55] PCM (probabilistic CM) + FCM [62] FCM + Level set [63] DWT + PNN [64,65] Level set + RG (region growing) [66] fully convolutional neural network (FCNNs) +…”

Section: Contour and Shape Based Techniquesmentioning

confidence: 99%

Survey of Brain Tumor Segmentation Techniques on Magnetic Resonance Imaging

Hameurlaine¹,

Moussaoui²

2019

Nano BioMed ENG

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Brain tumor extraction is challenging task because brain image and its structure are complicated that can be analyzed only by expert physicians or radiologist. Brain tumor detection and segmentation is one of the most challenging and time consuming task in medical image processing. The image segmentation is a very difficult job in the image processing and challenging task for clinical diagnostic tools. MRI (Magnetic Resonance Imaging) is a visualization medical technique, which provides plentiful information about the human soft tissue, which helps in the diagnosis of brain tumor. Accurate segmentation of the MRI images is extremely important and essential for the exact diagnosis by computer aided clinical tools. There are different types of segmentation algorithms for MRI brain images. This paper is to check existing approaches of Brain tumor segmentation techniques in MRI image for Computer aided diagnosis.

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Section: Contour and Shape Based Techniquesmentioning

confidence: 99%

Survey of Brain Tumor Segmentation Techniques on Magnetic Resonance Imaging

Hameurlaine¹,

Moussaoui²

2019

Nano BioMed ENG

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“…Probabilistic Neural Network Model PNN was used in this study (6)(7)(8)(9)(10). PNN is composed of four layers: input layer, radial base layer (pattern layer), decisionmaking layer (summation layer) and output layer ( Figure 1).…”

Section: Probabilistic Neural Networkmentioning

confidence: 99%

“…Probabilistic neural network (PNN) was first proposed by Donald F. Specht in the late 18 th century. The theoretical basis of the network is Bayesian classification theory and probability density function estimation (6)(7)(8)(9)(10). It can realize the function of nonlinear learning algorithms with linear learning algorithms, which is widely applied in pattern classification problems.…”

Section: Introductionmentioning

confidence: 99%

Classification of Metastatic and Non-Metastatic Thoracic Lymph Nodes in Lung Cancer Patients Based on Dielectric Properties Using Adaptive Probabilistic Neural Networks

Wang

et al. 2021

Front. Oncol.

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ObjectiveDielectric properties can be used in normal and malignant tissue identification, which requires an effective classifier because of the high throughput nature of the data. With easy training and fast convergence, probabilistic neural networks (PNNs) are widely applied in pattern classification problems. This study aims to propose a classifier to identify metastatic and non-metastatic thoracic lymph nodes in lung cancer patients based on dielectric properties.MethodsThe dielectric properties (permittivity and conductivity) of lymph nodes were measured using an open-ended coaxial probe. The Synthetic Minority Oversampling Technique method was adopted to modify the dataset. Feature parameters were scored to select the appropriate feature vector using a Statistical Dependency algorithm. The dataset was classified using adaptive PNNs with an optimized smooth factor using the simulated annealing PNN (SA-PNN). The results were compared with traditional Probabilistic, Support Vector Machines, k-Nearest Neighbor and the Classify functions in MATLAB.ResultsThe conductivity frequencies of 3959, 3958, 3960, 3978, 3510, 3889, 3888, and 3976 MHz were selected as the feature vectors for 219 lymph nodes (178 non-metastatic and 41 metastatic). Compared with the other methods, SA-PNN achieved the highest classification accuracy (92.92%) and the corresponding specificity and sensitivity were 94.72% and 91.11%, respectively.ConclusionsCompared with the other methods, the SA-PNN proposed in the present study achieved a higher classification accuracy, which provides a new scheme for classification of metastatic and non-metastatic thoracic lymph nodes in lung cancer patients based on dielectric properties.

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“…They have extracted the features using discrete wavelet transform and the features were reduced from 1024 to 7 by PCA. Further, in reference, [54] feature extraction task is done by using PCA and MRI image classification is carried out by PNN. A hybrid machine learning approach is used in [56] for brain tumor detection.…”

Section: E Artificial Neural Networkmentioning

confidence: 99%

Critical Analysis of Brain Magnetic Resonance Images Tumor Detection and Classification Techniques

Ullah¹,

Lee²,

An³

2020

IJACSA

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The image segmentation, tumor detection and extraction of tumor area from brain MR images are the main concern but time-consuming and tedious task performed by clinical experts or radiologist, while the accuracy relies on their experiences only. So, to overcome these limitations, the usage of computer-aided design (CAD) technology has become very important. Magnetic resonance imaging (MRI) and Computed Tomography (CT) are the two major imaging modalities that are used for brain tumor detection. In this paper, we have carried out a critical review of different image processing techniques of brain MR images and critically evaluate these different image processing techniques in tumor detection from brain MR images to identify the gaps and limitations of those techniques. Therefore, to obtain precise and better results, the gaps can be filled and limitations of various techniques can be improved. We have observed that most of the researchers have employed these stages such as Pre-processing, Feature extraction, Feature reduction, and Classification of MR images to find benign and malignant images. We have made an effort in this area to open new dimensions for the readers to explore the concerned field of research.

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Brain Tumor Classification using Principal Component Analysis and Probabilistic Neural Network

Cited by 32 publications

References 7 publications

Survey of Brain Tumor Segmentation Techniques on Magnetic Resonance Imaging

Survey of Brain Tumor Segmentation Techniques on Magnetic Resonance Imaging

Classification of Metastatic and Non-Metastatic Thoracic Lymph Nodes in Lung Cancer Patients Based on Dielectric Properties Using Adaptive Probabilistic Neural Networks

Critical Analysis of Brain Magnetic Resonance Images Tumor Detection and Classification Techniques

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