the brain responsible for managing thoughts, mem-ories, movements, and emotions, is the body's most critical organ but is vulnerable to tumors, particularly as we age. these tumors can cause structural changes and...
详细信息
ISBN:
(数字)9798331530952
ISBN:
(纸本)9798331530969
the brain responsible for managing thoughts, mem-ories, movements, and emotions, is the body's most critical organ but is vulnerable to tumors, particularly as we age. these tumors can cause structural changes and lead to life-threatening conditions, such as brain cancer. Early detection is essential for effective treatment and prevention of fatal outcomes. While CT and MRI scans are currently used to detect brain tumors, they still require expert analysis to pinpoint their exact location. To streamline and enhance the efficiency of tumor detection, we propose a Convolutional Neural Network (CNN)-based approach for analyzing MRI scans. Our model is designed to classify three types of brain tumors-pituitary, glioma, and meningioma-as well as cases with no tumors. Using gray scaling, One-hot encoding, and other preprocessing techniques, we developed a customized CNN model that processes 6484 segmented MRI images for training and testing. We tested the performance of seven pretrained models-Vgg16, Vgg19, ResNet50, InceptionV3, DenseNet-121, and MobileNet-on this dataset, achieving testing accuracies of 93.37 %, 92.42 %, 75.38 %, 91.48%, 94.89%, 23.30%, and 96.02%, respectively. Additionally, we created an ensemble model using all these architectures, resulting in a training accuracy of 91.36% and a validation accuracy of 91.29%. Our customized CNN model outperformed all others because of its architecture and proper hyperparameter tuning, achieving a testing accuracy of 98.11%. With significantly lower time complexity, our model can make reliable decisions in a short period, offering a fast and cost-effective solution. Despite integrating various parameters to manage adverse conditions, the system remains lightweight and efficient. In conclusion, our CNN-based approach provides superior accuracy and speed, making it a viable tool for early brain tumor detection.
image segmentation aims at partitioning an image into its constituent parts, which plays a crucial role in practical applications. In this paper, we present a wavelet frame-based model for color images segmentation, w...
详细信息
ISBN:
(纸本)9781728136608
image segmentation aims at partitioning an image into its constituent parts, which plays a crucial role in practical applications. In this paper, we present a wavelet frame-based model for color images segmentation, which can be regarded as a discretization to the classical Chan-Vese (C-V) model. the advantage of the wavelet frame-based approach is that it has fast algorithm and is able to extract important features of the input images. We then apply the alternating direction method of multipliers (ADAM) algorithm to solve the model. the experiments on some color image segmentation tasks indicate that our algorithm performs favorably against several existing methods.
Nonlinearities are often encountered in the analysis and processing of real-world signals. this paper develops new signal decompositions for nonlinear analysis and processing. the theory of tensor norms is employed to...
详细信息
ISBN:
(纸本)0819422134
Nonlinearities are often encountered in the analysis and processing of real-world signals. this paper develops new signal decompositions for nonlinear analysis and processing. the theory of tensor norms is employed to show that wavelets provide an optimal basis for the nonlinear signal decompositions. the nonlinear signal decompositions are also applied to signalprocessing problems.
this paper presents a stationary wavelet transform (SWT) method to de-noise the microarray images. It's well known that SWT is time invariant. Hence it makes particularly important in statistical signalprocessing...
详细信息
ISBN:
(纸本)0780376676
this paper presents a stationary wavelet transform (SWT) method to de-noise the microarray images. It's well known that SWT is time invariant. Hence it makes particularly important in statistical signalprocessingapplications, such as signal detection and de-noising. the testing result on sample microarray image has shown a clear and a better resolution image is obtained using this method.
Bivariate box splines for image interpolation, enhancement, digital filter design, subband coding bank, hexagonal filtering will be discussed. Some existing and new results will be presented. A computational method fo...
详细信息
ISBN:
(纸本)0819422134
Bivariate box splines for image interpolation, enhancement, digital filter design, subband coding bank, hexagonal filtering will be discussed. Some existing and new results will be presented. A computational method for box spline image interpolation and box spline digital filters are included.
Most digital signalprocessing methods have an underlying assumption of regularly-spaced data samples. However, many real-world data collection techniques generate data sets which are not sampled at evenly-spaced inte...
详细信息
ISBN:
(纸本)0819422134
Most digital signalprocessing methods have an underlying assumption of regularly-spaced data samples. However, many real-world data collection techniques generate data sets which are not sampled at evenly-spaced intervals, or which may have significant data dropout problems. therefore, a method of interpolation is needed to model the signal on an even grid of arbitrary granularity. We propose the interpolation of nonuniformly sampled fields using a least- square fit of the data to a wavelet basis in a multiresolution setting.
the objective of this paper is to investigate and provide insight into wavelet-based higher-order time-scale analysis, with an emphasis on the third-order statistic, the bispectrum. We also use a wavelet-based higher-...
详细信息
ISBN:
(纸本)0819422134
the objective of this paper is to investigate and provide insight into wavelet-based higher-order time-scale analysis, with an emphasis on the third-order statistic, the bispectrum. We also use a wavelet-based higher-order time- scale approach to detect and quantify intermittent phase- locking of first- and second-order components in laboratory generated random sea waves.
We investigate three approaches to VLSI implementation of wavelet filters. the direct form structure, the lattice form structure, and an algebraic structure are used to derive different architectures for wavelet filte...
详细信息
ISBN:
(纸本)0819422134
We investigate three approaches to VLSI implementation of wavelet filters. the direct form structure, the lattice form structure, and an algebraic structure are used to derive different architectures for wavelet filters. the algebraic structure exploits conjugacy properties in number fields. All approaches are explained in detail for the Daubechies 4- tab filters. We outline the philosophy of a design method for integrated circuits.
In this paper, we propose a method of efficient computation of wavelet coefficients from DCT-based coded image/video signals. Block transform domain filtering is well suited for transcoding of such data. First direct ...
详细信息
ISBN:
(纸本)0819422134
In this paper, we propose a method of efficient computation of wavelet coefficients from DCT-based coded image/video signals. Block transform domain filtering is well suited for transcoding of such data. First direct transform domain processing removes the necessary of inverse transform. Second, the number of nonzero elements in the blocks are significantly smaller than spatial domain. therefore, the amount of computation can be reduced accordingly. Finally, the block processing algorithm provides a parallel processing method. Hence a fast implementation of the algorithm is well suited.
A multiscale vision model based on a pyramidal wavelet transform is described in the present paper. the pyramidal wavelet algorithm is modified in order to satisfy a correct sampling at each scale. Objects are defined...
详细信息
暂无评论