A Novel and Hybrid Optimization Mechanism For Denoising And Classification Of Medical Images using DTCWPT And Neuro-Fuzzy Classifiers
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Computed tomography (CT) images are usually corrupted by several noises from the measurement process complicating the automatic feature extraction and analysis of clinical data. To attain the best possible diagnosis it is very vital that medical images be clear, sharp, and free of noise and artifacts. In this research paper, we propose a robust technique to denoise, detect and classify the tumour part from CT medical images. Our proposed approach consists of four phases, such as denoising, region segmentation, feature extraction and classification. In the denoising phase Dual Tree Complex Wavelet Packets and Empirical Mode Decomposition are used for removing noise. The performance of the proposed technique is assessed on the five CT images taking into consideration, Gaussian and salt & pepper noise for the parameters, PSNR and SDME. In the segmentation process K-means clustering technique is employed. For the feature extraction, the parameters contrast, energy and gain are extracted. In classification, a modified technique called Cuckoo-Neuro Fuzzy (CNF) algorithm is developed and applied for detection of the tumour region. The cuckoo search algorithm is employed for training the neural network and the fuzzy rules are generated for classification, according to the weights of the training sets. From the obtained outcomes, we can conclude that the proposed denoising technique have shown better values for the SDME of 69.9798, PSNR of 29.8413 for salt & pepper noise and an accuracy of 96.3% which is very superior compared to existing methods
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