Fossa ship rescue optimization-enabled 3D convolutional neural network for severity-level classification of brain tumor using 3D-MRI images

Detecting brain tumors early on is critical for effective treatment and life-saving efforts. The analysis of the brain with MRI scans is fundamental to the diagnosis because it contains detailed structural views of the brain, which is vital in identifying any of its abnormalities. The other option of performing an invasive biopsy is very painful and uncomfortable, which is not the case with MRI as it is free from surgically invasive margins and pieces of equipment. This helps patients to feel more at ease and hasten the diagnostic procedure, allowing physicians to formulate and practice action plans quicker. It is very difficult to locate a human brain tumor by manual because MRI scans produce large numbers of three-dimensional images. Complete applicability of pre-written computerized diagnostics, affords high possibilities in providing areas of interest earlier through the application of machine learning techniques and algorithms. The proposed work in the present study was to develop a deep learning model which will classify brain tumor grade images (BTGC), and hence enhance accuracy in diagnosing patients with different grades of brain tumors using MRI. A MobileNetV2 model, was used to extract the features from the images. This model increases the efficiency and generalizability of the model further. In this study, six standard Kaggle brain tumor MRI datasets were used to train and validate the developed and tested model of a brain tumor detection and classification algorithm into several types. This work consists of two key components: (i) brain tumor detection and (ii) classification of the tumor. The tumor classifications are conducted in both three classes (Meningioma, Pituitary, and glioma) and two classes (malignant, benign). The model has been reported to detect brain tumors with 99.85% accuracy, to distinguish benign and malignant tumors with 99.87% accuracy, and to type meningioma, pituitary, and glioma tumors with 99.38% accuracy. The results of this study indicate that the described technique is useful in the detection and classification of brain tumors.

Brain tumors pose a significant clinical challenge due to their aggressive nature and the complexity of accurate diagnosis. Traditional deep learning models, primarily based on Convolutional Neural Networks (CNNs), have shown promise in tumor segmentation and classification from Magnetic Resonance Imaging (MRI) scans. However, these approaches often struggle with capturing global spatial dependencies, generalizing from limited annotated data, and fine-tuning performance across varying imaging conditions. This paper presents an advanced hybrid framework that combines self-supervised learning, Vision Transformer (ViT), and Quantum Particle Swarm Optimization (QPSO) to enhance segmentation and classification of brain tumors. The methodology begins with preprocessing involving skull stripping, contrast enhancement, and denoising using a Cycle-Consistent Generative Adversarial Network (CycleGAN). Feature representation is learned through a Simple Framework for Contrastive Learning of Visual Representations (SimCLR), followed by segmentation using a DeepLabv3+ architecture with a ViT encoder. Tumor classification is handled by a lightweight ViT model, whose hyperparameters are optimized using QPSO. Evaluation on the Brain Tumor Segmentation (BraTS) 2021 dataset demonstrates a Dice Similarity Coefficient (DSC) of 95.4% and classification accuracy of 96.7%, confirming the robustness and superiority of the proposed approach over conventional methods.

Background.Molecular markers provide important biological and clinical information related to the classification of brain tumors, and the integration of relevant molecular parameters into brain tumor classification systems has been a widely discussed topic in neuro-oncology over the past decade. With recent advances in the development of clinically relevant molecular signatures and the 2016 World Health Organization (WHO) update, the views of the neuro-oncology community on such changes would be informative for implementing this process.Methods.A survey with 8 questions regarding molecular markers in tumor classification was sent to an email list of Society for Neuro-Oncology members and attendees of prior meetings (n=5065). There were 403 respondents. Analysis was performed using whole group response, based on self-reported subspecialty.Results.The survey results show overall strong support for incorporating molecular knowledge into the classification and clinical management of brain tumors. Across all 7 subspecialty groups, ≥70% of respondents agreed to this integration. Interestingly, some variability is seen among subspecialties, notably with lowest support from neuropathologists, which may reflect their roles in implementing such diagnostic technologies.Conclusion.Based on a survey provided to the neuro-oncology community, we report strong support for the integration of molecular markers into the WHO classification of brain tumors, as well as for using an integrated “layered” diagnostic format. While membership from each specialty showed support, there was variation by specialty in enthusiasm regarding proposed changes.The initial results of this survey influenced the deliberations underlying the 2016 WHO classification of tumors of the central nervous system.

A brain tumour is a deadly syndrome caused due to abnormal and uncontrolled expansion of extra cells that creates several tissues in the brain to affect the nervous system. It rapidly increases the growth of tumour cells and affects the brain by damaging or squeezing healthy tissues. Automatic brain tumour classification was done by conditional aquila horse herd optimization driven deep neuro fuzzy network (CAHO-based DNFN) based on MR image. First, image segmentation is done with a multi-encoder net framework (ME-Net), and features that involve statistical and convolutional neural network (CNN) features are extracted. Then, the ME-Net training is performed using AHO. Utilizing deep neuro fuzzy network (DNFN), which is trained by fusing CAViaR with AO and HOA, tumour classification is carried out utilizing augmented data after the process. The proposed scheme showed outstanding results with the measures, namely testing accuracy, specificity, and sensitivity that acquired the values of 0.915, 0.9 and 0.926, respectively.

Due to the complexity of the images and dearth of anatomical models, it is highly difficult to accurately represent the various deformations in each component of the medical images. In recent years, a significant number of children and adults have affected from brain tumors, which is one of the most terrible types of disease affects the people around the world. Moreover, the Magnetic Resonance Imaging (MRI) based brain tumor detection is one of a significant study area in the field of medical imaging. Since, the use of computerized methods aids in the detection and treatment of disease by the medical professionals. The development of an automated method for the accurate detection and classification of tumors from brain MRIs. In this framework, a tanh normalization process is used to smooth out the input brain MRIs with less noise artefacts and improved quality. Then, a group feature extraction model is used to extract the relevant features from the normalized image, which includes both Speeded Up Robust Features (SURF) and Grey Level Co-occurrence Matrix (GLCM) features. The Water Chaotic Fruitfly Optimization (WChFO) method is used to identify the best features for increasing the speed of classifier training and testing processes with less time. Moreover, a Deep Recurrent Neural Network (DRNN) model is used to classify the type of brain tumor for accurate early diagnosis and treatment. The most well-known benchmarking datasets, like BRATS and Kaggle, employed for analysis in order to assess the effectiveness and results of the proposed brain tumor diagnosis system. By using the proposed WChFO-DRNN technique, the accuracy of the tumor detection system is increased to 99.2% with the sensitivity, specificity of 99% and time consumption of 0.2s.

We present a holistic brain tumor screening and classification method for detecting and distinguishing multiple types of brain tumors on MR images. The challenges arise from the significant variations of location, shape, size, and contrast of these tumors. The proposed algorithms start with feature extraction from axial slices using dense convolutional neural networks; the obtained sequential features of multiple frames are then fed into a recurrent neural network for classification. Different from most other brain tumor classification algorithms, our framework is free from manual or automatic region of interests segmentation. The results reported on a public dataset and a population of 422 proprietary MRI scans diagnosed as normal, gliomas, meningiomas and metastatic brain tumors demonstrate the effectiveness and efficiency of our method.

Brain Age Estimation (BAE) has become a popular challenge in the field of medical and computer sciences in recent years. In the medical sciences field, the investigation on the brain structure and its relationship with aging is considerable. In the computer sciences field, creating an efficient Machine Learning (ML) model of BAE would lead to have accurate regression models. In this paper, a 3D Convolutional Neural Network (3D-CNN) model is used to train a brain age estimation system. To reach a more accurate system, two other regression methods are also applied on the final feature vector generated by 3D-CNN system. The system is applied on the samples of IXI dataset normalized by SPM14. Next, to ensure the model’s generalization, 47 healthy samples of ADNI1 dataset are used. Furthermore, some MRI images achieved from Alzheimer patients are feed to the proposed model and the effects of Alzheimer disease on brain aging are investigated. The best Mean Absolute Error (MAE) on evaluation dataset is about 5 years, with Root Mean Square Error (RMSE) = 13.5. The model generalization by a new healthy dataset was evaluated and the result is with the MAE value of about 6 years.

Brain tumor is a kind of cancer, in which tissues in the brain grows rapidly and unevenly in the brains and causes huge threats on human life. Brain tumor is recognized as one of the common dreadful cancers among adults and it also affects the children too. This kind of cancer is categorized into two types, such as benign tumor and malignant tumor. However, benign tumor is curable, whereas recovering of patients whoever affected by malignant tumor has less chance to survive. Nowadays, MR images are usually employed to detect the kinds of brain tumor. Early classification and identification of tumor is significant to treat the tumor and saves the human life from early death. However, the classification of brain tumor and percentage in change detection using pre-operative and post-operative MR images is a very challenging task. In order to overcome such issues, this research proposes a new effective technique for brain tumor classification and determination of pixel change detection using proposed Deep Belief Network (DBN) + Deep Convolutional Neural Network (DCNN). The process involves four phases, such as pre-processing, segmentation, feature extraction, and classification. The combination of DBN + CNN is employed for decision making based on error function. The DBN + CNN are trained utilizing the developed BirCat algorithm. Moreover, the proposed approach achieved a maximum accuracy of 0.957, sensitivity of 0.967, and specificity of 0.918.

We propose an accurate and fast classification network for classification of brain tumors in MRI images that outperforms all lightweight methods investigated in terms of accuracy. We test our model on a challenging 2D T1-weighted CE-MRI dataset containing three types of brain tumors: Meningioma, Glioma and Pituitary. We introduce an l <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> -normalized spatial attention mechanism that acts as a regularizer against overfitting during training. We compare our results against the state-of-the-art on this dataset and show that by integrating l <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> -normalized spatial attention into a baseline network we achieve a performance gain of 1.79 percentage points. Even better accuracy can be attained by combining our model in an ensemble with the pretrained VGG16 at the expense of execution speed. Our code is publicly available at https://github.com/juliadietlmeier/MRI_image_classification.

This study proposes the integration of attention modules, feature-fusion blocks, and baseline convolutional neural networks for developing a robust multi-path network that leverages its multiple feature-extraction blocks for non-hierarchical mining of important medical image-related features. The network is evaluated using 10-fold cross-validation on large-scale magnetic resonance imaging datasets involving brain tumor classification, brain disorder classification, and dementia grading tasks. The Attention Feature Fusion VGG19 (AFF-VGG19) network demonstrates superiority against state-of-the-art networks and attains an accuracy of 0.9353 in distinguishing between three brain tumor classes, an accuracy of 0.9565 in distinguishing between Alzheimer’s and Parkinson’s diseases, and an accuracy of 0.9497 in grading cases of dementia.

Deep convolutional neural network for automatic segmentation and classification of jaw tumors in contrast-enhanced computed tomography images

The complexity of brain tumors highlights the critical need for advanced computer‐aided diagnosis (CAD) tools to support surgeons in clinical decision‐making and improve patient outcomes. This paper introduces a novel deep learning model for the multiclass classification of brain tumors using magnetic resonance imaging (MRI), offering significant advancements in feature extraction and classification accuracy. The proposed model comprises three key components: (1) a fine‐tuned EfficientNetB7 convolutional neural network (CNN), adapted through transfer learning by freezing the initial layers and retraining subsequent layers to optimize feature extraction from MR images; (2) a channel attention module that refines extracted feature maps, emphasizing essential features for accurate tumor detection; and (3) a fully connected classifier, optimized through grid search, to achieve precise multiclass tumor classification. Additionally, hyperparameter tuning and data augmentation techniques enhance generalization and model robustness. Experimental results confirm the model’s superior performance, outperforming recent approaches in multiclass and binary classification scenarios, underscoring its potential to advance brain tumor diagnosis and treatment.

Dual-stage deep learning framework for brain tumor classification and localization using multimodal MRI scans

Segmentation and classification of brain tumor are time-consuming and challenging chore in clinical image processing. Magnetic Resonance Imaging (MRI) offers more information related to human soft tissues that assists in diagnosing brain tumor. Precise segmentation of the MRI images is vital to diagnose brain tumor by means of computer-aided medical tools. Afterwards suitable segmentation of MRI brain tumor images, tumor classification is performed that is a hard chore owing to complications. Therefore, Gannet Aquila Optimization Algorithm_deep maxout network (GAOA_DMN) and GAOA_K-Net+speech enhancement generative adversarial network (GAOA_K-Net+Segan) is presented for classification and segmentation of brain tumor utilizing MRI images. Here, pre-processing phase performs noise removal from input image utilizing the Laplacian filter and also the region of interest (ROI) extraction is also carried out. Then, segmentation of brain tumor is conducted by K-Net+Segan, which is combined by Motyka similarity. However, K-Net+Segan for segmentation is trained by GAOA that is an amalgamation of Gannet Optimization Algorithm (GOA) and Aquila Optimizer (AO). From segmented image, features are extracted for performing classification phase. At last, brain tumor classification is conducted by DMN, which is tuned by GAOA and thus, output is obtained. Furthermore, GAOA_K-Net+Segan obtained better outcomes in terms of segmentation accuracy whereas devised GAOA_DMN achieved maximum accuracy, true negative rate (TNR) and true positive rate (TPR) of 92.7%, 94.5% and 91.5%.

In this paper, we present a study on brain tumor detection using the VGG-16 model, a convolutional neural network known to be effective for computer vision tasks. This study proposes an enhanced VGG-16 model by modifying its architecture with batch normalization and dropout layers to improve generalization and prevent overfitting. Unlike previous studies that directly applied VGG-16, this research introduces architectural changes to adapt the model for more effective multi-class classification of brain tumors. The objective of this study is to accurately determine the presence or absence of a brain tumor by classifying magnetic resonance imaging (MRI) images. The dataset used consists of MRI images of brain tumors classified into two classes: No (no tumor) and YES (tumor). The methodology includes environment setup, data import and preprocessing, VGG16 model construction, and performance evaluation using metrics such as accuracy, precision, and recall. The results show an accuracy of about 98.47% on the validation set and 98.32% on the test set, indicating that the VGG-16 model has the potential to assist medical professionals in diagnosing brain tumors. This study contributes to the field of medical image analysis and provides ideas for applying deep learning to brain tumor diagnosis.