A brain tumor is a distorted tissue wherein cells replicate rapidly and indefinitely, with no control over tumor growth. Deep learning has been argued to have the potential to overcome the challenges associated with detecting and intervening in brain tumors. It is well established that the segmentation method can be used to remove abnormal tumor regions from the brain, as this is one of the advanced technological classification and detection tools. In the case of brain tumors, early disease detection can be achieved effectively using reliable advanced A.I. and Neural Network classification algorithms. This study aimed to critically analyze the proposed literature solutions, use the Visual Geometry Group (VGG 16) for discovering brain tumors, implement a convolutional neural network (CNN) model framework, and set parameters to train the model for this challenge. VGG is used as one of the highest-performing CNN models because of its simplicity. Furthermore, the study developed an effective approach to detect brain tumors using MRI to aid in making quick, efficient, and precise decisions. Faster CNN used the VGG 16 architecture as a primary network to generate convolutional feature maps, then classified these to yield tumor region suggestions. The prediction accuracy was used to assess performance. Our suggested methodology was evaluated on a dataset for brain tumor diagnosis using MR images comprising 253 MRI brain images, with 155 showing tumors. Our approach could identify brain tumors in MR images. In the testing data, the algorithm outperformed the current conventional approaches for detecting brain tumors (Precision = 96%, 98.15%, 98.41% and F1-score = 91.78%, 92.6% and 91.29% respectively) and achieved an excellent accuracy of CNN 96%, VGG 16 98.5% and Ensemble Model 98.14%. The study also presents future recommendations regarding the proposed research work.

Brain tumors present significant health risks due to abnormal cell growth, potentially leading to organ dysfunction and mortality in adults. Magnetic resonance imaging (MRI) is crucial for tumor classification, but limited expertise in this area necessitates advanced methods for accurate diagnosis. Deep Learning has emerged as a pivotal tool, yet gaps remain in achieving optimal accuracy. This study addresses these gaps by proposing an enhanced model for classifying meningioma, glioma, and pituitary gland tumors, thereby improving precision in brain tumor detection. Trained on a dataset of 5712 images, the model achieves exceptional accuracy (99%) in both training and validation datasets, with a focus on precision. Leveraging techniques such as data augmentation, transfer learning with ResNet50, and regularization ensures stability and generalizability. Evaluation on a 1311-image test set reveals outstanding class-specific accuracies (glioma: 98.33%, meningioma: 94.44%, no tumor: 100.00%, pituitary: 100.00%). Comprehensive metrics including precision (0.983559), recall (0.983219), F1 score (0.983140), and AUC (ROC) (0.999038) underscore the model’s efficacy. This study demonstrates the potential of deep learning in early brain tumor diagnosis, surpassing conventional methods and laying a robust foundation for future research in neural network-based classification algorithms for brain tumors.

Researchers have given immense consideration to unsupervised approaches because of their tendency for automatic feature generation and excellent performance with a reduced error margin. Deep learning (DL) models are emerging as vital methods for image analysis in medical fields, such as classification, segmentation, and reconstruction. Deep learning relies on learning hierarchical features and data representation, making it superior to its antecedent. Deep learning models efficiently discover descriptive information about the optimal representation of various brain tumors when applied for brain tumor classification from MRI. Despite various efforts, there remains a gap in the current literature for inclusive representation of recently developed deep learning-based classification methods. The current study attempts to fill this gap by briefly reviewing the current state of the art on brain tumor segmentation and classification methods while focusing on deep learning methods. the proposed survey dedicates itself to reviewed the current state of the art on automated classification techniques for brain tumor MRI to produce an inclusive picture of the most recent and worthy of adoption models proposed in this area. Despite various attempts to conduct surveys on brain tumor segmentation and classification techniques, no such study could be found in the current literature that has dedicated its focus to the most effective approach towards classification. This research begins by identifying major classes of brain tumor segmentation and classification while presenting its focused area and reviewing the most recent state-of-the-art classification approach, the deep learning-based classification method. The powerful learning ability of deep learning mechanisms has been reviewed for their performance, and a comparison between them is presented to encourage its applications. Future recommendations and directions are also drawn up to establish a pursuable course for welcoming widespread adoption of potential applications in the area.

Background: Parkinson’s disease (PD) is a chronic neurodegenerative disorder characterized by bradykinesia, tremor, and rigidity among other symptoms. With a 70% cumulative prevalence of dementia in PD, cognitive impairment and neuropsychiatric symptoms are frequent.Materials and methods: In this study, we looked at anatomical brain differences between groups of patients and controls. A total of 138 people with PD were compared to 64 age-matched healthy people using voxel-based morphometry (VBM). VBM is a fully automated technique that allows for the identification of regional differences in gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) allowing for an objective comparison of brains of different groups of people. We used statistical parametric mapping for image processing and statistical analysis.Results: In comparison to controls, PD patients had lower GM volumes in the left middle cingulate, left lingual gyrus, right calcarine and left fusiform gyrus, also PD patients indicated lower WM volumes in the right middle cingulate, left lingual gyrus, right calcarine, and left inferior occipital gyrus. Moreover, PD patients group demonstrated higher CSF in the left caudate compared to the controls.Conclusion: Physical fragility and cognitive impairments in PD may be detected more easily if anatomical abnormalities to the cingulate gyrus, occipital lobe and the level of CSF in the caudate are identified. Thus, our findings shed light on the role of the brain in PD and may aid in a better understanding of the events that occur in PD patients.

Objective: Schizophrenia (SZ) is a functional mental condition that has a significant impact on patients’ social lives. As a result, accurate diagnosis of SZ has attracted researchers’ interest. Based on previous research, resting-state functional magnetic resonance imaging (rsfMRI) reported neural alterations in SZ. In this study, we attempted to investigate if dynamic functional connectivity (dFC) could reveal changes in temporal interactions between SZ patients and healthy controls (HC) beyond static functional connectivity (sFC) in the cuneus, using the publicly available COBRE dataset. Methods: Sliding windows were applied to 72 SZ patients’ and 74 healthy controls’ (HC) rsfMRI data to generate temporal correlation maps and, finally, evaluate mean strength (dFC-Str), variability (dFC-SD and ALFF) in each window, and the dwelling time. The difference in functional connectivity (FC) of the cuneus between two groups was compared using a two-sample t-test. Results: Our findings demonstrated decreased mean strength connectivity between the cuneus and calcarine, the cuneus and lingual gyrus, and between the cuneus and middle temporal gyrus (TPOmid) in subjects with SZ. Moreover, no difference was detected in variability (standard deviation and the amplitude of low-frequency fluctuation), the dwelling times of all states, or static functional connectivity (sFC) between the groups. Conclusions: Our verdict suggest that dynamic functional connectivity analyses may play crucial roles in unveiling abnormal patterns that would be obscured in static functional connectivity, providing promising impetus for understanding schizophrenia disease.

An accurate and efficient automatic brain tumor segmentation algorithm is important for clinical practice. In recent years, there has been much interest in automatic segmentation algorithms that use convolutional neural networks. In this paper, we propose a novel hierarchical multi-scale segmentation network (HMNet), which contains a high-resolution branch and parallel multi-resolution branches. The high-resolution branch can keep track of the brain tumor’s spatial details, and the multi-resolution feature exchange and fusion allow the network’s receptive fields to adapt to brain tumors of different shapes and sizes. In particular, to overcome the large computational overhead caused by expensive 3D convolution, we propose a lightweight conditional channel weighting block to reduce GPU memory and improve the efficiency of HMNet. We also propose a lightweight multi-resolution feature fusion (LMRF) module to further reduce model complexity and reduce the redundancy of the feature maps. We run tests on the BraTS 2020 dataset to determine how well the proposed network would work. The dice similarity coefficients of HMNet for ET, WT, and TC are 0.781, 0.901, and 0.823, respectively. Many comparative experiments on the BraTS 2020 dataset and other two datasets show that our proposed HMNet has achieved satisfactory performance compared with the SOTA approaches.