Brain Image Classification Accuracy Enhancement Using Combined DWT and Enhanced CNN Approach
Keywords:
Brain diseases, Image classification, Discrete Wavelet Transform, Convolutional Neural Network, Medical imaging, Early diagnosis.Abstract
Globally, brain illnesses are associated with substantial health consequences, underscoring the need of early diagnosis and treatment. In order to aid in early diagnosis, a number of algorithms have been investigated, with an emphasis on utilizing imaging methods like CT and MRI. In order to improve the classification accuracy of brain images, this research suggests a unique method that combines an augmented convolutional neural network (CNN) with the discrete wavelet transform (DWT). In order to increase the accuracy of early diagnosis of brain illnesses, the goal of this project is to provide a strong categorization framework for brain image analysis. This entails using cutting-edge methods to problems like feature extraction, classification, and noise reduction. This work is innovative in that it uses an improved CNN architecture for classification after DWT is included for pre-processing and feature extraction. The framework efficiently eliminates noise and improves image accuracy by utilizing DWT, which helps to contribute to more dependable illness detection. Furthermore, by including an improved CNN, accuracy is further improved by introducing a unique way to using deep learning for brain image categorization. The three primary phases of the suggested framework are feature extraction, classification, and pre-processing. During pre-processing, noise from salt and pepper is eliminated by using a median filter, which is then converted to grayscale. The next step is to use DWT with a 3-level Haar wavelet to reduce edge dimensional space and improve image accuracy. The converted photos are used for feature extraction, which gets them ready for classification. Lastly, for precise brain image categorization, an improved CNN is used. The results show that the suggested framework achieves a remarkable accuracy rate of 98.9% in brain image categorization. This illustrates how the upgraded CNN and combined DWT methodology outperforms current techniques in reliably diagnosing brain disorders.
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