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

Accurate brain tumor detection and localization are essential for timely diagnosis and treatment in medical imaging. In this paper, we propose an advanced deep learning model using Convolutional Neural Networks (CNN) integrated with ResNet-101 for brain tumor detection, tumor localization, and classification of missing modality images. The model leverages the residual learning capabilities of ResNet-101 for effective feature extraction, enhancing the detection of brain tumors even in incomplete or missing modality. Additionally, a localization module is incorporated to pinpoint the tumor region, improving interpretability for clinical use. Results demonstrate that the CNN-ResNet-101 model excels in tumor detection and classification, maintaining high accuracy across both complete and missing modality images. Furthermore, the model's ability to localize tumors without compromising classification performance is highlighted through improved precision and recall scores. The proposed model achieves better performance for robust brain tumor detection and localization in challenging medical imaging conditions. The proposed model is evaluated and benchmarked against other techniques, with a comprehensive performance comparison using key metrics such as accuracy, precision, recall, and F1-score.

Classification of brain tumors is of great importance in medical applications that benefit from computer-aided diagnosis. Misdiagnosis of brain tumor type will both prevent the patient from responding effectively to the applied treatment and decrease the patient’s chances of survival. In this study, we propose a solution for classifying brain tumors in MR images using transfer learning networks. The most common brain tumors are detected with VGG16, VGG19, ResNet50 and DenseNet21 networks using transfer learning. Deep transfer learning networks are trained and tested using four different optimization algorithms (Adadelta, ADAM, RMSprop and SGD) on the accessible Figshare dataset containing 3064 T1-weighted MR images from 233 patients with three common brain tumor types: glioma (1426 images), meningioma (708 images) and pituitary (930 images). The area under the curve (AUC) and accuracy metrics were used as performance measures. The proposed transfer learning methods have a level of success that can be compared with studies in the literature; the highest classification performance is 99.02% with ResNet50 using Adadelta. The classification result proved that the most common brain tumors can be classified with very high performance. Thus, the transfer learning model is promising in medicine and can help doctors make quick and accurate decisions.

Accurate diagnosis of the brain tumor type at an earlier stage is crucial for the treatment process and helps to save the lives of a large number of people worldwide. Because they are non-invasive and spare patients from having an unpleasant biopsy, magnetic resonance imaging (MRI) scans are frequently employed to identify tumors. The manual identification of tumors is difficult and requires considerable time due to the large number of three-dimensional images that an MRI scan of one patient's brain produces from various angles. Moreover, the variations in location, size, and shape of the brain tumor also make it challenging to detect and classify different types of tumors. Thus, computer-aided diagnostics (CAD) systems have been proposed for the detection of brain tumors. In this paper, we proposed a novel unified end-to-end deep learning model named TumorDetNet for brain tumor detection and classification. Our TumorDetNet framework employs 48 convolution layers with leaky ReLU (LReLU) and ReLU activation functions to compute the most distinctive deep feature maps. Moreover, average pooling and a dropout layer are also used to learn distinctive patterns and reduce overfitting. Finally, one fully connected and a softmax layer are employed to detect and classify the brain tumor into multiple types. We assessed the performance of our method on six standard Kaggle brain tumor MRI datasets for brain tumor detection and classification into (malignant and benign), and (glioma, pituitary, and meningioma). Our model successfully identified brain tumors with remarkable accuracy of 99.83%, classified benign and malignant brain tumors with an ideal accuracy of 100%, and meningiomas, pituitary, and gliomas tumors with an accuracy of 99.27%. These outcomes demonstrate the potency of the suggested methodology for the reliable identification and categorization of brain tumors.

Brain tumor is a severe nervous disorder that causes damage to health and often leads to death. Therefore, it is significant to classify the brain tumor at an early stage as it increases the survival rate of patients. One of the commonly employed imaging modalities for brain tumor classification is Magnetic Resonance Imaging (MRI). However, it is relatively complex to perform the brain tumor classification process due to the variations of type, shape, size and tumor location. To overcome such issues and classify the tumor more accurately, a deep learning classifier named Deep Maxout network is developed to classify the tumor into different grades. Based on the classification result, the features connected with the tumor grades are effectively acquired to make the survival prediction process. Deep learning is an effective and robust classifier model employed to perform the tumor classification or detection process with the MRI modality. Here, the survival prediction of tumor patients is carried out by the Deep Long Short-Term Memory (LSTM) classifier. Accordingly, the proposed method achieved higher performance using accuracy, sensitivity, specificity and prediction error with the values of 0.9434, 0.9324, 0.9202 and 0.0579.

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.

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.

Radiomics Prediction of Radiation Treatment Outcomes in Oropharyngeal Cancer: A Clinical and Image Repository in Concert with the Cancer Imaging Archive (TCIA)

Enhanced performance of Dark-Nets for brain tumor classification and segmentation using colormap-based superpixel techniques

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%.

The comprehensive molecular profiling of cancer has resulted in new insights into the biology and classification of numerous tumor types. In the case of primary brain tumors that commonly affect adults, an emerging set of disease-defining biomarker sets is reshaping existing diagnostic entities that had previously been defined on the basis of their microscopic appearance. Substantial progress has been made in this regard for common primary brain tumors in adults, especially diffuse gliomas, where large-scale profiling efforts have led to the incorporation of highly prevalent molecular alterations that promote a biologically based classification as an adjunct to the traditional histopathologic approach. The growing awareness that histologically indistinguishable tumors can be divided into more precise and biologically relevant subgroups has demanded a more global routine approach to biomarker assessment. These considerations have begun to intersect with the decreasing costs and availability of genome-wide analysis tools and, thus, incorporation into routine practice. We review how molecular profiling already has led to an evolution in the classification of brain tumors. In addition, we discuss the likely trajectory of incorporation of global molecular profiling platforms into the routine clinical classification of adult brain tumors.

The use of mobile phones and the risk of brain tumors among children and adolescents

Accurate classification of brain tumors is a major challenge in neuro-oncology, as the heterogeneity of tumor morphology and the overlap of radiological features limit the effectiveness of conventional diagnostic approaches. Early and reliable tumor characterization is essential for treatment planning, prognosis, and improved patient outcomes. Recent advances in artificial intelligence (AI) have enabled the development of deep learning frameworks that can augment radiological interpretation and support clinical decision-making. This study proposes and validates a hybrid computational framework that integrates convolutional neural networks (CNNs) with graph convolutional networks (GCNs) for automated classification of brain tumors from magnetic resonance imaging (MRI). A publicly available Kaggle-based MRI dataset was utilized, consisting of four categories: glioma, meningioma, pituitary tumor, and no-tumor. The proposed pipeline incorporated systematic preprocessing, transfer learning via the InceptionV3 architecture for hierarchical feature extraction, graph construction to model inter-feature relationships, and GCN-based relational learning for final classification. Hyperparameter optimization was performed using Particle Swarm Optimization (PSO) to improve generalizability. The experimental evaluation achieved an overall classification accuracy of 92.91%. Class-specific performance analysis demonstrated particularly high diagnostic accuracy in the no-tumor group (F1-score: 0.9963) and pituitary tumor group (F1-score: 0.9599). The incorporation of PSO tuning further improved the validation accuracy to 94.23%. The hybrid CNN-GCN framework exhibited robustness against imaging artifacts and irregular tumor boundaries, conditions that commonly challenge conventional classification techniques. The integration of CNN-based hierarchical feature extraction with GCN-based relational reasoning provides a significant advancement in automated brain tumor classification. This reproducible and intelligent diagnostic pipeline demonstrates strong potential for clinical translation by enhancing diagnostic precision, reducing radiologist workload, and facilitating timely therapeutic interventions. The findings support the integration of graph-based deep learning systems into smart healthcare ecosystems, where AI-assisted diagnostic tools can contribute to improved outcomes in neuro-oncology.

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.

Brain tumors are abnormal growth of cells which are very serious issue, and the early diagnosis is the main factor for the enhancement of the patients' situation. The traditional methods of brain tumor detection and classification are usually time-consuming, and often the human error is involved. Also, they cannot at all accurately classify the different types of tumors. The deep learning models mainly CNNs have been the main factors for the significant transformation of the medical image analysis. CNNs are applied to automatically extract the complex features from MRI images which make it possible to get the advantages of the exact identification of the tumor boundaries and the slight variations that can be ignored by human. This work investigates the use of transfer learning technique to improve the accuracy of CNN models for brain tumor detection and classification. We tested VGG16 and ResNet50 models on 4000 MRI images with tumor and no-tumor. Both scratch and pre-trained models were used. Results show that pre-trained VGG16 and ResNet50 achieved 99.44% and 99.55% accuracy while scratch models achieved 59.64% and 96.02% accuracy respectively. This result proves the power of transfer learning technique in deep learning models especially CNNs for brain tumor detection and classification.

Brain tumor classification using medical images is crucial for identification and therapy. However, brain tumors are complex and vary, making grouping them difficult. This work demonstrates a novel transfer learning method for brain tumor classification. We employ trained Convolutional Neural Networks (CNNs) models and data enrichment approaches to extract meaningful information from medical images. We want to fine-tune the models built on our dataset to uncover hierarchical patterns that distinguish tumor types. Through data enrichment, the training sample becomes more diverse and richer, making the model more generic and robust. Our team's extensive testing and research have shown that the suggested procedure can identify brain tumors. Our machine-learning approach performs better than others in terms of accuracy, sensitivity, specificity, and precision. Our technique improves brain tumor categorization and assures accurate clinical diagnosis. Automated testing systems are one way for physicians to assist patients in selecting the best course of treatment. Researchers may improve classification performance by incorporating modern imaging technology or topic-specific data. The Internet of Things, or IoT, is helping to drive the development of complex real-time data collection, processing, and sharing systems. These technological advancements have transformed medical imaging. This graphic depicts a cutting-edge transfer learning system that may be able to identify brain cancer from medical photos. This technology has the potential to enhance data collection and processing via the Internet of Things. Data augmentation and pre-trained convolutional neural networks may help to extract interpretable medical images. The Internet of Things improved the model's flexibility, resilience, and utility. We achieved this by expanding the training data set. Rapid categorization advancements have made clinical diagnosis more efficient. Classification, deep learning, medical imaging, machine learning, transfer learning, tumor detection, and image analysis all relate to this topic.