Probabilistic Neural Network for Brain Tumor Classification

Othman, Mohd Fauzi; Basri, Mohd Ariffanan Mohd

doi:10.1109/isms.2011.32

Cited by 101 publications

(51 citation statements)

References 6 publications

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“…For example to separate between the blood vessel class and the fat class, three extracted features, narrowness and histogram consistency, were used as the inputs to the ANN [34]. Other medical applications of ANNs include disease diagnosis such as liver cancer detection [35], locating automatically the outline of the lungs in MRI images of the thorax [36], Brain tumour classification [37], and in abnormal retinal image classification [38], An example is lung cancer detection by using ANN and fuzzy C-Mean clustering algorithm [39]. This was a promising field to apply in the classification of WBC differentiated bone marrow cells.…”

Section: 1literature Surveymentioning

confidence: 99%

Review on Feature Extraction and Classification of WBC in Bone Marrow for Disease Diagnosis

Pergad¹,

S.T.²

2016

International Journal of Innovative Research in Electrical, Ele

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Abstract:In this paper, we make a review on different features and classification methods for white blood cells in bone marrow for disease diagnosis. A new set of features based on the wavelet and Radon transform to bone marrow blood cell differential classification solution. The transform based novel features are the coefficient values of decomposition for horizontal, vertical & diagonal factors with two levels. the factors where the coefficient values are of six in numbers the first and second level coefficient method to the classifiers by the desired output using a previous information of the number of coefficients as a samples in each class. Artificial neural networks Levenberg-Marguardt (LM) back propagation algorithm for validation is applied in the experiments. The results are used in various disease diagnoses.

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Section: 1literature Surveymentioning

confidence: 99%

Review on Feature Extraction and Classification of WBC in Bone Marrow for Disease Diagnosis

Pergad¹,

S.T.²

2016

International Journal of Innovative Research in Electrical, Ele

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show abstract

“…Principal component analysis had been done in Othman and Basri (2011), after which the classification was carried out using probabilistic neural network. In their work, it was observed that the PNN classifier showed good accuracy along with less training time.…”

Section: Jcsmentioning

confidence: 99%

Brain Tumor Classification Based on Clustered Discrete Cosine Transform in Compressed Domain

Anitha¹,

Murugavalli²

2014

Journal of Computer Science

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This study presents a novel method to classify the brain tumors by means of efficient and integrated methods so as to increase the classification accuracy. In conventional systems, the problem being the same to extract the feature sets from the database and classify tumors based on the features sets. The main idea in plethora of earlier researches related to any classification method is to increase the classification accuracy.The actual need is to achieve a better accuracy in classification, by extracting more relevant feature sets after dimensionality reduction. There exists a trade-off between accuracy and the number of feature sets. Hence the focus in this study is to implement Discrete Cosine Transform (DCT) on the brain tumor images for various classes. Using DCT, by itself, it offers a fair dimension reduction in feature sets.Later on, sequentially K-means algorithm is applied on DCT coefficients to cluster the feature sets. These cluster information are considered as refined feature sets and classified using Support Vector Machine (SVM) is proposed in this study. This method of using DCT helps to adjust and vary the performance of classification based on the count of the DCT coefficients taken into account. There exists a good demand for an automatic classification of brain tumors which grealtly helps in the process of diagnosis. In this novel work, an average of 97% and a maximum of 100% classification accuracy has been achieved. This research is basically aiming and opening a new way of classification under compressed domain. Hence this study may be highly suitable for diagnosing under mobile computing and internet based medical diagnosis.

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“…Considering that more than one type of fault may co-exist at the same time, it may be significant to propose a classifier which could offer the probabilities of all possible faults. In order to realize this assumption, the probabilistic neural network (PNN) [33,34] is employed as a probabilistic classifier. It is testified that the performance of PNN is superior to the SVM based method [29].…”

Section: Introductionmentioning

confidence: 99%

Representational Learning for Fault Diagnosis of Wind Turbine Equipment: A Multi-Layered Extreme Learning Machines Approach

2016

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Reliable and quick response fault diagnosis is crucial for the wind turbine generator system (WTGS) to avoid unplanned interruption and to reduce the maintenance cost. However, the conditional data generated from WTGS operating in a tough environment is always dynamical and high-dimensional. To address these challenges, we propose a new fault diagnosis scheme which is composed of multiple extreme learning machines (ELM) in a hierarchical structure, where a forwarding list of ELM layers is concatenated and each of them is processed independently for its corresponding role. The framework enables both representational feature learning and fault classification. The multi-layered ELM based representational learning covers functions including data preprocessing, feature extraction and dimension reduction. An ELM based autoencoder is trained to generate a hidden layer output weight matrix, which is then used to transform the input dataset into a new feature representation. Compared with the traditional feature extraction methods which may empirically wipe off some "insignificant' feature information that in fact conveys certain undiscovered important knowledge, the introduced representational learning method could overcome the loss of information content. The computed output weight matrix projects the high dimensional input vector into a compressed and orthogonally weighted distribution. The last single layer of ELM is applied for fault classification. Unlike the greedy layer wise learning method adopted in back propagation based deep learning (DL), the proposed framework does not need iterative fine-tuning of parameters. To evaluate its experimental performance, comparison tests are carried out on a wind turbine generator simulator. The results show that the proposed diagnostic framework achieves the best performance among the compared approaches in terms of accuracy and efficiency in multiple faults detection of wind turbines.

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Probabilistic Neural Network for Brain Tumor Classification

Cited by 101 publications

References 6 publications

Review on Feature Extraction and Classification of WBC in Bone Marrow for Disease Diagnosis

Review on Feature Extraction and Classification of WBC in Bone Marrow for Disease Diagnosis

Brain Tumor Classification Based on Clustered Discrete Cosine Transform in Compressed Domain

Representational Learning for Fault Diagnosis of Wind Turbine Equipment: A Multi-Layered Extreme Learning Machines Approach

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