Brain MR Image Classification Using DWT and Random Forest with AdaBoostM1 Classifier

Krishna, Garrepally Gopi; Ashwin, S.; Karuna, Yepuganti; Saladi, Saritha

doi:10.1109/iccsp.2018.8524180

Cited by 3 publications

(1 citation statement)

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“…For dimensionality reduction, the different PF (axes) is found incrementally. The PCA is computationally very efficient method and one of the most common forms of factor analysis 20,21 . It is a linear projection method employed to decrease the number of parameters by transferring a set of correlated variables into uncorrelated variables.…”

Section: Methodsmentioning

confidence: 99%

Segmentation of tumor using PCA based modified fuzzy C means algorithms on MR brain images

Karuna

Saritha²,

Narasimhulu³

2020

Int J Imaging Syst Tech

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In the field of medical sciences, automatic detection of tumor using magnetic resonance (MR) brain images is a major research area. The goal of the proposed work is to identify the tumors in MR images using segmentation methods and to locate the affected regions of the brain more accurately. Medical images have vast information but they are difficult to examine with lesser computational time. An innovative process is proposed to extract tumor cells using the discrete wavelet transform (DWT). After extracting features with DWT feature reduction is carried out with the principal component analysis (PCA). Modified fuzzy C means (MFCM) technique is used for segmenting the tumor cells. The efficiency of the proposed method to identify different abnormalities in real MR images for intracranial neoplasm detection, tuberculoma, and bilateral thalamic fungal granulomas identification is tested. The results obtained are shown in‐terms of Accuracy, Dice Similarity Index (DSI), and Jaccard Index (JI) measures. The performance of the proposed method is tested in terms of performance measures like Accuracy, DSI, and JI. These results are compared with the conventional fuzzy C means (FCM) method.

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Section: Methodsmentioning

confidence: 99%