An Effective Classification Methodology for Brain MRI Classification Based on Statistical Features, DWT and Blended ANN

Fayaz, Muhammad; Haider, Jawad; Qureshi, Muhammad Bilal; Qureshi, Muhammad Shuaib; Habib, Sami J.; Gwak, Jeonghwan

doi:10.1109/access.2021.3132159

Cited by 8 publications

(3 citation statements)

References 20 publications

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“…It includes noise removal with morphological techniques. The results are checked with a confusion matrix [10]. The methodology of M. Fayaz et al does not apply the confusion matrix on a pixel-by-pixel basis, unlike what is done here.…”

Section: E Comparison With Other Workmentioning

confidence: 99%

Noise Estimation for MRI Images with Revised Theory on Histograms of Second-order Derivatives

Chan

2023

IJORAS

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Previous research by the author in the use of histograms of second-order derivatives showed that the differences between pixels in MRI images can be determined without referring to the ground truth for the purpose of noise reduction. Yet, the results of the previous research also showed that the methodologies used could not prevent false positives and negatives. To address this problem, a technique has been developed to involve multiple conditions that utilize the statistics of the histograms and the circumstances of neighbours in the vicinity of a pixel. The confusion matrix for this method shows that the technique has marginal but consistent improvement across the noise levels that are tested, compared to prior methods

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Section: E Comparison With Other Workmentioning

confidence: 99%

Noise Estimation for MRI Images with Revised Theory on Histograms of Second-order Derivatives

Chan

2023

IJORAS

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“…Fayaz et al [49] proposed a model to do the binary classification of brain MRI. Firstly, the grayscale MRI images are converted into RGB images.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Fayaz et al [49] pproposed a model to do the binary classification of brain MRI. Achieved 98% accuracy.…”

Section: Referencementioning

confidence: 99%

PRCnet: An Efficient Model for Automatic Detection of Brain Tumor in MRI Images

Farhan,

Khalid,

Manzoor

2023

Preprint

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Brain tumors are the most prevalent and life-threatening cancer; an early and accurate diagnosis of brain tumors increases the chances of patient survival and treatment planning. However, manual tumor detection is a complex, cumbersome and time-consuming task and is prone to errors, which relies on the radiologist’s experience. As a result, the development of accurate and automatic system for tumor detection is critical. In this paper, we proposed a new model called Parallel Residual Convolutional Network (PRCnet) model to classify brain tumors from Magnetic Resonance Imaging. The PCRnet model uses several techniques (such as filters of different sizes with parallel layers, connections between layers, batch normalization layer, and ReLU) and dropout layer to overcome the over-fitting problem, for achieving accurate and automatic classification of brain tumors. The PRCnet model is trained and tested on two different datasets and obtained an accuracy of 94.77% and 97.1% for dataset A and dataset B, respectively which is way better as compared to the state-of-the-art models.

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An Efficient Classification Techniques for Brain Tumor Using Features Extraction and Statistic Methods, with Machine Learning Algorithms

Badshah

Farhatullah

Khan

et al. 2023

Lecture Notes on Data Engineering and Communications Technologies

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An Effective Classification Methodology for Brain MRI Classification Based on Statistical Features, DWT and Blended ANN

Cited by 8 publications

References 20 publications

Noise Estimation for MRI Images with Revised Theory on Histograms of Second-order Derivatives

Noise Estimation for MRI Images with Revised Theory on Histograms of Second-order Derivatives

PRCnet: An Efficient Model for Automatic Detection of Brain Tumor in MRI Images

An Efficient Classification Techniques for Brain Tumor Using Features Extraction and Statistic Methods, with Machine Learning Algorithms

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