You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Short Survey on MRI Brain Detection 
Chapter Title:  INTRODUCTION 
1. Abstract of Cited Paper (BIB001):  We propose an automatic and accurate technique for classifying normal and abnormal magnetic resonance (MR) images of human brain. Ripplet transform Type-I (RT), an e-cient multiscale geometric analysis (MGA) tool for digital images, is used to represent the salient features of the brain MR images. The dimensionality of the image representative feature vector is reduced by principal component analysis (PCA). A computationally less expensive support vector machine (SVM), called least square-SVM (LS-SVM) is used to classify the brain MR images. Extensive experiments were carried out to evaluate the performance of the proposed system. Two benchmark MR image datasets and a new larger dataset were used in the experiments, consisting 66, 160 and 255 images, respectively. The generalization capability of the proposed technique is enhanced by 5 £ 5 cross validation procedure. For all the datasets used in the experiments, the proposed system shows high classiflcation accuracies (on an average > 99%). Experimental results and performance comparisons with state-of-the-art techniques, show that the proposed scheme is e-cient in brain MR image classiflcation. 
2. Abstract of Cited Paper (BIB002):  Importance Impaired mitochondrial function impacts many biological processes that depend heavily on energy and metabolism and can lead to a wide range of neurodevelopmental disorders, including autism spectrum disorder (ASD). Although evidence that mitochondrial dysfunction is a biological subtype of ASD has grown in recent years, no study, to our knowledge, has demonstrated evidence of mitochondrial dysfunction in brain tissue in vivo in a large, well-defined sample of individuals with ASD. Objectives To assess brain lactate in individuals with ASD and typically developing controls using high-resolution, multiplanar spectroscopic imaging; to map the distribution of lactate in the brains of individuals with ASD; and to assess correlations of elevated brain lactate with age, autism subtype, and intellectual ability. Design, Setting, and Participants Case-control study at Columbia University Medical Center and New York State Psychiatric Institute involving 75 children and adults with ASD and 96 age- and sex-matched, typically developing controls. Main Outcomes and Measures Lactate doublets (present or absent) on brain magnetic resonance spectroscopic imaging. Results Lactate doublets were present at a significantly higher rate in participants with ASD (13%) than controls (1%) ( P = .001). In the ASD group, the presence of lactate doublets correlated significantly with age ( P = .004) and was detected more often in adults (20%) than in children (6%), though it did not correlate with sex, ASD subtype, intellectual ability, or the Autism Diagnostic Observation Schedule total score or subscores. In those with ASD, lactate was detected most frequently within the cingulate gyrus but it was also present in the subcortical gray matter nuclei, corpus callosum, superior temporal gyrus, and pre- and postcentral gyri. Conclusions and Relevance In vivo brain findings provide evidence for a possible neurobiological subtype of mitochondrial dysfunction in ASD.

Literature Review:
Magnetic resonance imaging (MRI) is capable of producing highquality images of the anatomical structures of the human body (especially the brain), while it is a low-risk, fast, non-invasive imaging technique.The rich information it provides is of great help for clinical diagnosis and biomedical research BIB003 . However, because of this huge informationrepository associated with MRIs,employing manual methods to interpret each image is tedious and time consuming, and it is of necessity to develop automateddiagnosis tools to draw quicker and easier inferences from the MRimages.These automated tools can benefit themedical personnel in diagnosis, prognosis, pre-surgical and postsurgical procedures, etc. BIB002 . Sophisticated signal/image processing techniques and some computational intelligent techniquesare applied to develop diagnostic tools. In one possible methodology, the problem of automaticclassification of normal/pathological subjects, based on brainMRIs, can be configured as a three-stage problem:1) feature extraction; 2) feature reduction; 3) classification BIB001 . The main contribution of this study is toreview the most recent classification algorithmsand their state-of-the-art. We summarized the advantages anddisadvantages of the reviewed algorithms in tables to provide astructured vision aspects involved in these algorithms. This paper is organized as follows: Section 2 presents the recent classification algorithms and their experiment results. Section 3 discusses the limitations of these methods, while the conclusion and future work are presented in Section 4.