Brain Disorders Identification by Machine Learning Classifiers

—Segmenting tumor from MRI images is an essential but time consuming manual duty. Performing an automatic segmentation is a defying task since different forms of tumor tissue exist for diverse patients and in many cases the tumor is similar to the normal tissue. Various studies proposed earlier to handle the issue of precisely segmenting the tumor but they discard the degradations and their effect to the precision of the segmentation. This article provides a more precise segmentation process through the use of appropriate pre-processing algorithms. The authors studied many enhancement and restoration algorithms and selected the NL-means, Laplacian filter and histogram equalization to be used as preprocessing techniques. Experimental results showed that using a suitable preprocessing scheme would produce a better segmentation process. Index Terms — Medical image processing, Tumor segmentation, MRI images, Non-local Means, Histogram equalization, Laplacian filter. I. INTRODUCTION A tumor is a mass of tissue that grows out of control of the normal forces that regulates growth. Brain tumor has a higher mortality rate when compared to more common types of cancer. This underlines the need of accurate prognosis of brain tumor in health care industry [1]. MRI has become a widely-used method of high-quality medical imaging, especially in brain imaging [2]. Image segmentation refers to the process of partitioning a digital image into multiple segments, also known as super pixels. Image segmentation is an important process in computer vision and image processing, which divides an image into a number of disjoint regions such that the pixels have high similarity in order to use its different fields; science, medical, agriculture and industrial fields [3]. Besides, Magnetic Resonance Imaging (MRI) images are known to be corrupted by noise [4], blur [5] and low contrast artifact [6]. Therefore, this article discusses the issue of achieving a better segmentation through the use of proper image restoration and enhancement techniques. Moreover, the accurate and automatic segmentation of a brain tumor on MRI image is of great interest for assessing tumor growth and treatment responses, enhancing computer-assisted surgery, and constructing tumor growth models [7]. For those reasons, an automatic and reliable tumor segmentation method would be a useful tool since the medical image segmentation plays an instrumental role in clinical diagnosis [8]. The rest of the article sections are organized as the following: in section two, a brief elucidation about some related studies is presented as the used literature review. Moreover, detailed explanations about the employed algorithms are delivered in section three as a methodology. Also, the results of the proposed methodology are illustrated in section four as the experimental results. Likewise, a detailed discussion about the use of certain algorithms and its results is deliberated in section five. Finally, a brief closure about this article is provided in section six as a conclusion. II. LITERATURE REVIEW In this section, three recent studies are briefly described. The first study was presented by [9] which proposed a texture based analysis to identify irregularity in the brain and automatic segmentation using seeded region growing segmentation. The method of tumor detection based on the texture of the MRI is proposed to segment the abnormality from the regular adjacent, although small quantities misclassified, but the segmentation result is precise. Moreover, [10] suggested a strong technique for automatic detection of tumor for brain MRI images. He also proposed a new morphological technique which is used for the stripped skull region from the brain images. Detecting tumor precisely based on seeded region growing segmentation on an improved single seed point selection algorithm is

Abstract: This work explores a novel approach to accurately identify brain tumors from magnetic resonance imaging (MRI) images by utilizing the complementary capabilities of fuzzy C-Means (FCM) clustering and support vector machines (SVM). Using the soft clustering approach, MRI images are meticulously segregated in the first stage, known as FCM clustering. By giving individual voxels varying degrees of membership, FCM deftly handles the inherent ambiguities and complexity of medical pictures to produce a nuanced representation of tissue kinds. SVM, a potent supervised learning technique for binary classification applications, comes after segmentation. uses this information in the second phase of classification. Through the creation of a hyperplane that divides tumor and non-tumor regions to the greatest extent possible, support vector machines (SVM) are able to optimize the margin between these classes and improve their ability to generalize. We deliberately integrate FCM clustering with SVM classification in order to take use of the complementary characteristics that each of these techniques possesses. While SVM's strong classification skills enable it to fine-tune the identification of tumor regions, FCM's sophisticated segmentation captures the spatial distribution of tissues.

Brain tumor detection and segmentation from Magnetic Resonance Imaging (MRI) images is being one of the emerging fields in the biomedicine. A formidable undertaking in brain tumor surgery, medical care, treatment programme and quantitative assessment of MRI images is to precisely diagnose its location and extent. Recently, the convolutional neural network (CNN) based detection and segmentation method on brain tumor MRI images is being one of the emerging fields in the medical imaging as an automatic clinic treatment and evaluation solution. In this article, we put forward a brand new quadruplet loss in CNN framework, which achieves higher accuracy in brain tumor detection and segmentation than other pairwise loss and triplet loss methods. By applying the proposed quadruplet loss to the original L2Net CNN architecture leads to a more compact descriptor named QuadrupletNet. From our experiments, QuadrupletNet shows higher performance than other state-of-the-art loss functions e.g., the Triplet loss, as indicated in experiments on Multimodal Brain Tumor Image Segmentation (BRATS 2018) datasets, and on our own collected MRI brain tumor datasets (named MBTD).

We sought to examine the intra- and interobserver variability in measuring the cochlear duct length (CDL) from magnetic resonance imaging (MRI) images versus computed tomography (CT) images using an otological surgical planning software that uses measurements of the basal turn diameter and cochlear width to estimate the CDL. Twenty-one adult cochlear implant patients with preoperative MRI and CT images. Three fellowship-trained neurotologists served as the raters in the study. One rater measured the CDL using preoperative CT scans to serve as the benchmark. Two of the raters measured the CDL on preoperative MRI scans. One rater also remeasured the scans using MRI images after a period of 1 week to assess intraobserver variability. Intraclass correlational coefficients were calculated to assess for intra- and interobserver agreement. The mean CDL measured from the CT scans was 32.7 ± 2.0 mm (range 29.4 - 37.6 mm). The mean difference between the raters when measuring the CDL using MRI scans was -0.15 ± 2.1 mm (range -3.2 to 4.3 mm). The intraclass correlational coefficients for inter-rater reliability of CDL determination using MRI scans was judged as fair to excellent (0.68; 95% CI 0.41-0.84). The intrarater reliability of CDL determination using MRI scans was judged at fair to excellent (0.73; 95% CI 0.491-0.866). We demonstrate that a validated otological surgical planning software for estimating the CDL preoperatively had comparable performance using MRI scans versus the gold-standard CT scans.

Brain tumor is the phenomenal growth of abnormal tissues in the human brain. A malignant tumor is known as a cancerous cell, and it is the major cause of death among people across the globe. Nowadays, the detection and classification of brain tumors from Magnetic Resonance Images (MRI) is a very crucial task. This chapter addresses various computing methods such as Edge Detection (ED), feature extraction, and classification techniques for the detection and classification of brain tumor regions from MRI datasets. Initially, MRI images are collected, and then various pre-processing steps such as filtering and edge detection are applied. Then, different edge detection methods such as Sobel ED, Robert’s ED, Prewitt ED, Canny ED, Laplacian ED, and Laplacian of Gaussian (LoG) with sigma 3 are applied for the MRI datasets. Subsequently, segmentation techniques are applied for detecting tumor regions from MRI, and essential features are extracted using Discrete Wavelet Transform (DWT) method. Otsu’s thresholding and K-means clustering segmentation methods are used for the investigation. Further, the support vector machines (SVMs), Naïve Bayes (NB), K-nearest neighbors (KNN), Back Propagation Neural Network (BPNN), and feedforward neural networks (FFNN) classifiers are employed for the MRI classification purpose. The experiments are 146conducted using the brain tumor dataset in the MATLAB 2019a software environment. The experimental results are analyzed in multiple dimensions, and it shows that the SVM with Otsu’s thresholding method exhibits better performance with 86.11% accuracy during the classification.

Aim This study aims to investigate whether machine learning (ML) could identify the volumetric changes in the corpus callosum (CC) and its sub-regions (genu, body, and splenium) due to aging and sex differences. Material and Methods Brain magnetic resonance imaging (MRI) images obtained from 301 healthy male and female subjects were used in the study. The volume measurements of the corpus callosum and its sub-regions were calculated from MRI images using MRICloud software. The classifications of age (young/adult) and sex (female/male) were performed using the Classification Learner Tool in MATLAB 2020b (MathWorks, Natick, MA, USA). Classifiers including k-Nearest Neighbor (KNN), support vector machine (SVM), decision tree, naïve Bayes, logistic regression, and Ensemble classifiers were evaluated using 10-fold cross-validation. Results Machine learning classification revealed moderate accuracy in distinguishing sex (best: Fine Gaussian SVM, 65.4% accuracy, AUC = 0.60) and higher accuracy in distinguishing age groups (young/adult; best: Fine Gaussian SVM, 83.7% accuracy, area under the curve (AUC) = 0.67) based on corpus callosum and its sub-region volumes derived from MRI. Conclusion This preliminary data suggests that ML can provide indications to assess the impact of age and sex on corpus callosum volume based on MRI data. The observed classification accuracies, particularly for sex, suggest that larger datasets are needed to enhance the accuracy of these models in future investigations.

Alzheimer's Disease (AD) is one of the most common neurodegenerative diseases, as projected in the related Magnetic Resonance Imaging (MRI). The early identification of AD is essential for preventive treatment; thus, different machine/deep learning (ML/DL) approaches applied on MRI scans from patients at different AD stages have been proposed in recent years. Here, a new method, namely CurvMRI, for AD detection from MRI images using Fast Curvelet Transform (FCT) is proposed. The approach is realized via a sequence of steps, i.e., feature extraction, feature reduction, and classification. MRI images are obtained from a Kaggle dataset containing five AD stages, from where Cognitive Normal (CN) (493/87 (training/testing)) and AD (145/26) MRI images were selected for binary classification. The FCT with wrapping method was implemented, and higher-order statistics, such as kurtosis and skewness, as well as energy and variance, were then used to extract features from the curvelet sub-bands. Features were then concatenated and fed to a Support Vector Machine (SVM) classifier, giving an accuracy of 77.6%, which outperforms the most common DL classification approaches applied to the same dataset. These results showcase the potentiality of the proposed CurvMRI to efficiently discriminate AD from CN in MRI images, and provide a fast and easy to implement ML tool for assisting physicians in AD detection.

Accurate brain tumor segmentation from magnetic resonance imaging (MRI) images is important for proper medication. Manual segmentation may be erroneous and a computer-aided method is recommended for precise segmentation which is also challenging due to the contrast level of MRI images. This research work proposes to utilize a parametric active contour model (PACM)-based deformable snake model to segment brain tumors from MRI images. Conventional PACM model prerequisites some initial points for its initialization which may have a time-consuming issue. The main contribution of this paper is to modify the PACM algorithm, so that it can predict its initial points around the region of interest (ROI) from the given minimum (at least three) initial points. This proposed method aids PACM to find the initial contour points automatically to start the deformable mechanism. Furthermore, different parameters of the PACM algorithm are optimized for the segmentation by check and trial method. The proposed method is applied to different shapes of brain tumors inside the MRI images and found satisfactory segmentation outcomes. Furthermore, the proposed algorithm reports the number of total pixels inside the segmented area. Therefore, we hope that this proposal will help to find the area of critically shaped brain tumor in an MRI image.

This study was to analyze the clinical application value of magnetic resonance imaging (MRI) image features based on intelligent algorithms in the diagnosis and treatment of breast cancer and to provide an effective reference assessment for breast cancer diagnosis. The MRI diagnosis model (ACO-MRI) based on the ant colony algorithm (ACO) was proposed, which was compared with the diagnosis methods based on support vector machine (SVM) and proximity (KNN) algorithm, and the proposed algorithm was applied to MRI images to diagnose breast cancer. The results showed that the accuracy, sensitivity, and specificity of the ACO-MRI model were greater than those of the KNN and SVM algorithm. Moreover, the specificity was statistically considerable compared with the two algorithms of KNN and SVM ( P < 0.05 ). By comparing 1/5 number of ants and the average gray path of the ACO-MRI model under 1/8 number of ants, it was found that the average gray path value of 1/8 number of ants was greatly higher than the average gray path value of 1/5 number of ants ( P < 0.05 ). The differences in the overall distribution of breast MRI imaging features among Luminal A, Luminal B, HER-2 overexpression, and TN were compared. There were considerable differences in the overall distribution of the three breast MRI imaging features of the boundaries, morphology, and enhancement methods among the four groups ( P < 0.05 ). In short, MRI image based on the intelligent algorithm ACO-MRI diagnosis model can effectively improve the diagnosis effect of breast cancer. Its image feature boundaries, morphology, and enhancement methods had good imaging features in the diagnosis of breast cancer.

Here, a brain tumor classification method using the support vector machine (SVM) algorithm by utilizing discrete wavelet transform (DWT) transformation and feature extraction of gray-level co-occurrence matrix (GLCM) and local binary pattern (LBP) has been implemented using the magnetic resonance imaging (MRI) image belong to the low-grade glioma (LGG) or high-grade glioma (HGG) group. SVM algorithm used as a classification method has been widely used in research that raises the topic of classification. Through the formation of a hyperplane between 2 data classes, the SVM algorithm can be said to be a reliable method but does not require complicated computations. The DWT transformation is intended to provide clearer feature details from the MRI image, so that when the feature extraction algorithm is applied, it is expected that the extracted features will differ between benign tumor MRI images and malignant tumor MRI images. In 1 level DWT using high-low (HL) sub-band yield the highest specificity, sensitivity, and accuracy than using 3 levels using HL or low-high (LH) sub-band in LGG MRI image.Compared with another research, our proposed method is slightly better in terms of accuracy to classify the brain tumor image with achieved the accuracy of 98.6486%.

Objective To investigate the value of computed tomography (CT)-magnetic resonance imaging (MRI) image fusion in target volume delineation of postoperative precise radiotherapy for gliomas. Methods Thirty-six patients newly diagnosed with gliomas were enrolled and received postoperative intensity-modulated radiotherapy. Landmark (L) combined with manual fusion (M) and maximization of mutual information (MI) was used for image fusion. Target volume and organs at risk were delineated based on CT images and fused images, respectively. Comparison of the volume was made by pairwise t test. The distance from a landmark on CT image to the corresponding one on MRI image was calculated. The volume method and geometric center method were used to calculate the degree of volume overlap and changes in central positions after image fusion. Results The L+ M+ MI method achieved a high registration accuracy in image fusion, with a registration error less than 2 mm. In patients with grade Ⅲ-IV gliomas, the CT images yielded significantly smaller gross tumor volume (GTV) and clinical target volume (CTV) than the MRI images (74.62±46.91 vs. 105.99±58.86 cm3,P=0.042; 304.03±130.05 vs. 387.94±150.12 cm3,P=0.040). After image fusion, the smallest change in central position occurred in the chiasma (1.32±1.42 mm), and the largest change occurred in CTV (7.99±11.06 mm), followed by GTV and the brain stem. Conclusions CT-MRI image fusion helps to reduce the uncertainty of target volume delineation in patients with gliomas, especially in those with edema and residual tumor after surgery. Key words: Tomography, X-ray computed; Magnetic resonance imaging; Image fusion; Brain neoplasms/radiotherapy

A new clinical diagnosis system for detecting brain tumor using integrated ResNet_Stacking with XGBoost

HQNet: A hybrid quantum network for multi-class MRI brain classification via quantum computing

Non-invasive imaging technologies, such as magnetic resonance imaging (MRI) and optical multimodality imaging methods, are commonly used for diagnosing and supervising the development of inflammatory bowel disease (IBD). These in vivo imaging methods can provide morphology changes information of IBD in macro-scale. However, it is difficult to investigate the intestinal wall in molecular and cellular level. State-of-art light-sheet and two-photon microscopy have the ability to acquire the changes for IBD in micro-scale. The aim of this work is to evaluate the size of the enterocoel and the thickness of colon wall using both MRI for in vivo imaging, and light-sheet and two-photon microscope for in vitro imaging. C57BL/6 mice were received 3.5% Dextran sodium sulfate (DSS) in the drinking water for 5 days to build IBD model. Mice were imaged with MRI on days 0, 6 to observe colitis progression. After MRI imaging, the mice were sacrificed to take colons for tissue clearing. Then, light-sheet and two-photon microscopies are used for in vitro imaging of the cleared samples. The experimental group showed symptoms of bloody stools, sluggishness and weight loss. It showed that the colon wall was thicker while the enterocoel was narrower compare to control group. The more details are observed using light-sheet and two-photon microscope. It is demonstrated that hybrid of MRI in macro-scale and light-sheet and two-photon microscopy in micro-scale imaging is feasible for colon inflammation diagnosing and supervising.

An accurate and early diagnosis of brain tumors based on medical imaging modalities is of great interest because brain tumors are a harmful threat to a person’s health worldwide. Several medical imaging techniques have been used to analyze brain tumors, including computed tomography (CT) and magnetic resonance imaging (MRI). CT provides information about dense tissues, whereas MRI gives information about soft tissues. However, the fusion of CT and MRI images has little effect on enhancing the accuracy of the diagnosis of brain tumors. Therefore, machine learning methods have been adopted to diagnose brain tumors in recent years. This paper intends to develop a novel scheme to detect and classify brain tumors based on fused CT and MRI images. The proposed approach starts with preprocessing the images to reduce the noise. Then, fusion rules are applied to get the fused image, and a segmentation algorithm is employed to isolate the tumor region from the background to isolate the tumor region. Finally, a machine learning classifier classified the brain images into benign and malignant tumors. Computing statistical measures evaluate the classification potential of the proposed scheme. Experimental outcomes are provided, and the Enhanced Flower Pollination Algorithm (EFPA) system shows that it outperforms other brain tumor classification methods considered for comparison.