An Efficient Methodology for Brain MRI Classification Based on DWT and Convolutional Neural Network

Fayaz, Muhammad; Torokeldiev, Nurlan; Turdumamatov, Samat; Qureshi, Muhammad Shuaib; Qureshi, Muhammad Bilal; Gwak, Jeonghwan

doi:10.3390/s21227480

Cited by 21 publications

(8 citation statements)

References 69 publications

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“…However, they utilized a small dataset for the experiments, and the system did not show significant performance as well. An integrated system was developed by [ 34 ], which was based on the Haar wavelet transform and convolutional neural network. They utilized a median filter for enhancement and multilevel Haar wavelet transform for feature extraction.…”

Section: Related Workmentioning

confidence: 99%

A Precise Medical Imaging Approach for Brain MRI Image Classification

Ṣiddiqi

Alsayat

Alhwaiti

et al. 2022

Computational Intelligence and Neuroscience

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Magnetic resonance imaging (MRI) is an accurate and noninvasive method employed for the diagnosis of various kinds of diseases in medical imaging. Most of the existing systems showed significant performances on small MRI datasets, while their performances decrease against large MRI datasets. Hence, the goal was to design an efficient and robust classification system that sustains a high recognition rate against large MRI dataset. Accordingly, in this study, we have proposed the usage of a novel feature extraction technique that has the ability to extract and select the prominent feature from MRI image. The proposed algorithm selects the best features from the MRI images of various diseases. Further, this approach discriminates various classes based on recursive values such as partial Z-value. The proposed approach only extracts a minor feature set through, respectively, forward and backward recursion models. The most interrelated features are nominated in the forward regression model that depends on the values of partial Z-test, while the minimum interrelated features are diminished from the corresponding feature space under the presence of the backward model. In both cases, the values of Z-test are estimated through the defined labels of the diseases. The proposed model is efficiently looking the localized features, which is one of the benefits of this method. After extracting and selecting the best features, the model is trained by utilizing support vector machine (SVM) to provide the predicted labels to the corresponding MRI images. To show the significance of the proposed model, we utilized a publicly available standard dataset such as Harvard Medical School and Open Access Series of Imaging Studies (OASIS), which contains 24 various brain diseases including normal. The proposed approach achieved the best classification accuracy against existing state-of-the-art systems.

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Section: Related Workmentioning

confidence: 99%

A Precise Medical Imaging Approach for Brain MRI Image Classification

Ṣiddiqi

Alsayat

Alhwaiti

et al. 2022

Computational Intelligence and Neuroscience

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“…Sarhan [ 3 ] introduced wavelet transformation and CNN based-brain tumor detection and classification, but this method has produced no indication about the classification of the distorted image. Fayaz et al [ 5 ] proposed DWT and CNN-based brain MRI classification methods, but this method was not able to identify the problem of rotated and scaled brain MRI images. Suganya et al [ 6 ] described a method of geometric distortion of brain MRI for tumor detection and segmentation, but there was no guideline for rotated and scaled brain MRI images.…”

Section: Related Workmentioning

confidence: 99%

“…Classification of brain MRI images plays a vital role in the analysis and interpretation of brain diseases. Many methods have been proposed to design an accurate classifier to distinguish between normal and abnormal brain MRIs [ 3 , 4 , 5 , 6 ]. Feature extraction is a prominent process extensively used to classify brain MRIs.…”

Section: Introductionmentioning

confidence: 99%

Cancerous and Non-Cancerous Brain MRI Classification Method Based on Convolutional Neural Network and Log-Polar Transformation

et al. 2022

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Magnetic resonance imaging (MRI) offers visual representations of the interior of a body for clinical analysis and medical intervention. The MRI process is subjected to a variety of image processing and machine learning approaches to identify, diagnose, and classify brain diseases as well as detect abnormalities. In this paper, we propose an improved classification method for distinguishing cancerous and noncancerous tumors from brain MRI images by using Log Polar Transformation (LPT) and convolutional neural networks (CNN). The LPT has been applied for feature extraction of rotation and scaling of distorted images, while the integration of CNN introduces a machine learning approach for the tumor classification of distorted images. The dataset was formed with images of seven different brain diseases, and the training set was formed by applying CNN with the extracted features. The proposed method is then evaluated in comparison to state-of-the-art algorithms, showing a definite improvement of the former. The obtained results show that the machine learning approach offers better classification with a success rate of about 96% in both plain brain MR images and rotation- and scale-invariant brain MR images. This work also successfully classified T-1 and T-2 weighted images of neoplastic and degenerative brain diseases. The obtained accuracy is perfected by several kernel procedures, while the combined performance of the two wavelet transformations and a strong dataset make our method robust and efficient. Since no earlier study on machine learning approaches with rotated and scaled brain MRI has come to our attention, it is expected that our proposed method introduces a new paradigm in this research field.

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“…The idea of this method is that if the neural network is successfully trained on examples, then when new scattering signals are fed to the input of the network, the trained system can create the desired image directly on the basis of the acquired knowledge, without solving the complex inverse scattering problem. Another area of application of learning neural networks is their use for image recognition, classification, and segmentation [ 22 , 23 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

Application of the Model of Spots for Inverse Problems

Simonov

2023

Sensors

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This article proposes the application of a new mathematical model of spots for solving inverse problems using a learning method, which is similar to using deep learning. In general, the spots represent vague figures in abstract “information spaces” or crisp figures with a lack of information about their shapes. However, crisp figures are regarded as a special and limiting case of spots. A basic mathematical apparatus, based on L4 numbers, has been developed for the representation and processing of qualitative information of elementary spatial relations between spots. Moreover, we defined L4 vectors, L4 matrices, and mathematical operations on them. The developed apparatus can be used in Artificial Intelligence, in particular, for knowledge representation and for modeling qualitative reasoning and learning. Another application area is the solution of inverse problems by learning. For example, this can be applied to image reconstruction using ultrasound, X-ray, magnetic resonance, or radar scan data. The introduced apparatus was verified by solving problems of reconstruction of images, utilizing only qualitative data of its elementary relations with some scanning figures. This article also demonstrates the application of a spot-based inverse Radon algorithm for binary image reconstruction. In both cases, the spot-based algorithms have demonstrated an effective denoising property.

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An Efficient Methodology for Brain MRI Classification Based on DWT and Convolutional Neural Network

Cited by 21 publications

References 69 publications

A Precise Medical Imaging Approach for Brain MRI Image Classification

A Precise Medical Imaging Approach for Brain MRI Image Classification

Cancerous and Non-Cancerous Brain MRI Classification Method Based on Convolutional Neural Network and Log-Polar Transformation

Application of the Model of Spots for Inverse Problems

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