Diagnosis Of Brain Tumors Research Articles

Deep and Machine Learning Algorithms for Diagnosing Brain Cancer and Tumors

In the rapidly evolving field of medical diagnostics, the integration of deep learning (DL) and machine learning (ML) technologies has dramatically advanced the accuracy and efficiency of brain cancer and tumor diagnosis using magnetic resonance imaging (MRI). This review explores the transformative impact of these technologies, highlighting their role in enhancing tumor detection, classification, and early diagnosis interventions. DL and ML algorithms have significantly improved the analysis of complex imaging data, enabling more precise and faster diagnostic decisions, which are crucial for effective patient management and treatment planning. This review encompasses a broad spectrum of studies that illustrate the capabilities of these computational techniques in handling large datasets, learning intricate patterns, and achieving a high diagnostic performance. By delving into various algorithmic approaches and their clinical implications, this study underscores the importance of continued advancements and the integration of AI technologies in the field of oncology, aiming to foster better patient outcomes through innovative diagnostic tools.

Enhancing brain tumor classification in MRI scans with a multi-layer customized convolutional neural network approach.

The necessity of prompt and accurate brain tumor diagnosis is unquestionable for optimizing treatment strategies and patient prognoses. Traditional reliance on Magnetic Resonance Imaging (MRI) analysis, contingent upon expert interpretation, grapples with challenges such as time-intensive processes and susceptibility to human error. This research presents a novel Convolutional Neural Network (CNN) architecture designed to enhance the accuracy and efficiency of brain tumor detection in MRI scans. The dataset used in the study comprises 7,023 brain MRI images from figshare, SARTAJ, and Br35H, categorized into glioma, meningioma, no tumor, and pituitary classes, with a CNN-based multi-task classification model employed for tumor detection, classification, and location identification. Our methodology focused on multi-task classification using a single CNN model for various brain MRI classification tasks, including tumor detection, classification based on grade and type, and tumor location identification. The proposed CNN model incorporates advanced feature extraction capabilities and deep learning optimization techniques, culminating in a groundbreaking paradigm shift in automated brain MRI analysis. With an exceptional tumor classification accuracy of 99%, our method surpasses current methodologies, demonstrating the remarkable potential of deep learning in medical applications. This study represents a significant advancement in the early detection and treatment planning of brain tumors, offering a more efficient and accurate alternative to traditional MRI analysis methods.

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

The cancer disease prediction and detection processes are crucial tasks in this emerging world and it is tough to manage the diseases. Generally, the disease prediction processes are done by using various kinds of inputs including numerical data and Magnetic Resonance Imaging (MRI) images. In the past, the researchers use the segmentation and classification methods on MRI images to enrich performance of the disease prediction system. However, these input images are using for the prediction, detection and diagnosis with reasonable delay and less prediction accuracy. In this work, we propose a new brain tumor disease prediction and diagnosis system that incorporates the segmentation and classification techniques for predicting and detecting the cancer diseases effectively. Here, new data pre-processing methods like mean fusion, automated cropping of the Region of Interest (ROI), and Gaussian filtering are used for performing effective classification. Moreover, the proposed system performs the standardized resizing and rescaling processes over the MRI images and it also automate the cropping process of the ROI to ensure consistent input sizes. In addition, the brain tumor segmentation process is performed by applying the LinkNet architecture with a SEResNeXt101 backbone network. Finally, the system focuses on brain tumor classification using ensemble model which combines the ResNet architectures through stacking, leveraging the strengths of individual models to improve classification accuracy in the first stage, and it incorporates the XGBoost algorithm to enhance the performance further in efficient manner. The proposed disease prediction system is assessed through experiments on MRI images. Finally, the system is proved as superior to other available disease prediction systems with respect to sensitivity, specificity, AUC and obtained 95.84% as overall prediction accuracy.

Long-read sequencing for brain tumors.

Brain tumors and genomics have a long-standing history given that glioblastoma was the first cancer studied by the cancer genome atlas. The numerous and continuous advances through the decades in sequencing technologies have aided in the advanced molecular characterization of brain tumors for diagnosis, prognosis, and treatment. Since the implementation of molecular biomarkers by the WHO CNS in 2016, the genomics of brain tumors has been integrated into diagnostic criteria. Long-read sequencing, also known as third generation sequencing, is an emerging technique that allows for the sequencing of longer DNA segments leading to improved detection of structural variants and epigenetics. These capabilities are opening a way for better characterization of brain tumors. Here, we present a comprehensive summary of the state of the art of third-generation sequencing in the application for brain tumor diagnosis, prognosis, and treatment. We discuss the advantages and potential new implementations of long-read sequencing into clinical paradigms for neuro-oncology patients.

Predominant localization and ways of metastatic dissemination to the brain of malignant tumors of the female reproductive system

Background. Metastases of the female reproductive system cancer to the brain are rare and reach about 5 % of all cases with metastatic lesions of the central nervous system. The most common metastases are ovarian cancer (0.49–6.1 %), endometrial cancer (0.4–1.2 %) and cervical cancer (0.3–0.9 %). The predominant localization and ways of spreading metastases to the brain in this category of tumors may differ from lung cancer, breast cancer, kidney cancer or melanoma.Aim. To analyze the localization of brain metastases of malignant tumors of the female reproductive system and the ways of dissemination of malignant cells from the primary focus to the central nervous system.Materials and methods. From 2013 to 2020, a total number of 448 patients with metastatic brain tumors from different cancers were operated on at the Department of Neuro‑Oncology of the Federal Center of Neurosurgery (Novosibirsk). Metastases of tumors of the female reproductive system were presented in 32 (7.1 %) cases. The average age was 55.1 (27–72) years. Ovarian cancer was the primary focus in 24 (5.3 %) cases, endometrial cancer – in 6 (1.3 %), cervical cancer – in 2 (0.4 %). Occipital lobe was the most often location for the metastases in the brain and occurred in 10 (31.3 %) patients. Subtentorial localization was on the second place and noted in 9 (28.1 %) cases. Of these, metastases to the cerebellum were registered in 8 cases and in 1 case the metastasis was located in the brain stem.Results. Gross total removal of metastasis was achieved in all 32 patients. Of 9 cases with subtentorial localization of metastasis, only 1 patient had lung metastases verified. Regression of neurological symptoms and improvement of the condition were noted in 16 patients after surgery. The Karnovsky performance score at the time of discharge was 85.2.Conclusion. The subtentorial location of metastases in malignant tumors of the female reproductive system occupies one of the leading places. This is because the dissemination of tumor cells from the pelvis can occur through a Batson vertebral venous system directly to the cerebellum or brain stem without the pulmonary blood circulation and lung dissemination. This should be taken into account for the early diagnosis of subtentorial metastatic brain tumors and referral of these patients for neurosurgical treatment.

Diagnosis of brain metastasis due to lung cancer and affecting factors at “Dr. Cipto Mangunkusumo” General Hospital

Background and objectives. The incidence of brain metastases is higher than primary brain tumors, with lung cancer as a common etiology at “Dr. Cipto Mangunkusumo” General Hospital. Delay in diagnosis can cause brain herniation, resulting in disability and death. We aimed to investigate the compatibility between daily clinical practice and clinical practice guidelines for diagnosing brain metastases due to lung cancer at “Dr. Cipto Mangunkusumo” General Hospital while also examining factors that might influence the duration of the diagnosis. Materials and methods. We conducted a retrospective cohort study to determine the conformity between the duration of diagnosis of metastatic brain tumors due to lung cancer in daily clinical practice with clinical practice guidelines. Secondary data was obtained from medical records from November 2021 until June 2022. Results. Twelve subjects (30%) were diagnosed with brain metastases from lung cancer within two weeks with a median duration of 18.5 days (IQR 12-34 days). The duration of 7 days (IQR 4-11 days) was required to obtain a lung mass, 8 days (IQR 4.5-13 days) to perform a biopsy, and 6 days (IQR 3.5-7 days) to obtain t pathology results. No statistically significant relationship exists between the variables assessed and the duration of diagnosis. Conclusions. The clinical practice guidelines at “Dr. Cipto Mangunkusumo” General Hospital for diagnosing brain metastases from lung cancer within two weeks could only be carried out in 30% of subjects in this study. Collaboration between departments is needed to make the diagnosis faster.

The Role of ArtificiaI Intelligence in Brain Tumor Diagnosis: An Evaluation of a Machine Learning Model.

This research study explores of the effectiveness of a machine learning image classification model in the accurate identification of various types of brain tumors. The types of tumors under consideration in this study are gliomas, meningiomas, and pituitary tumors. These are some of the most common types of brain tumors and pose significant challenges in terms of accurate diagnosis and treatment. The machine learning model that is the focus of this study is built on the Google Teachable Machine platform (Alphabet Inc., Mountain View, CA). The Google Teachable Machine is a machine learning image classification platform that is built from Tensorflow, a popular open-source platform for machine learning. The Google Teachable Machine model was specifically evaluated for its ability to differentiate between normal brains and the aforementioned types of tumors in MRI images. MRI images are a common tool in the diagnosis of brain tumors, but the challenge lies in the accurate classification of the tumors. This is where the machine learning model comes into play. The model is trained to recognize patterns in the MRI images that correspond to the different types of tumors. The performance of the machine learning model was assessed using several metrics. These include precision, recall, and F1 score. These metrics were generated from a confusion matrix analysis and performance graphs. A confusion matrix is a table that is often used to describe the performance of a classification model. Precision is a measure of the model's ability to correctly identify positive instances among all instances it identified as positive. Recall, on the other hand, measures the model's ability to correctly identify positive instances among all actual positive instances. The F1 score is a measure that combines precision and recall providing a single metric for model performance. The results of the study were promising. The Google Teachable Machine model demonstrated high performance, with accuracy, precision, recall, and F1 scores ranging between 0.84 and 1.00. This suggests that the model is highly effective in accurately classifying the different types of brain tumors. This study provides insights into the potential of machine learning models in the accurate classification of brain tumors. The findings of this study lay the groundwork for further research in this area and have implications for the diagnosis and treatment of brain tumors. The study also highlights the potential of machine learning in enhancing the field of medical imaging and diagnosis. With the increasing complexity and volume of medical data, machine learning models like the one evaluated in this study could play a crucial role in improving the accuracy and efficiency of diagnoses. Furthermore, the study underscores the importance of continued research and development in this field to further refine these models and overcome any potential limitations or challenges. Overall, the study contributes to the field of medical imaging and machine learning and sets the stage for future research and advancements in this area.

Improving the performance for automated brain tumor classification on magnetic resonance imaging deep learningbased

<p>Brain tumor is an uncontrolled growth of abnormal cell in the brain. Early diagnosis of brain tumor has a crucial step in this type of cancer, which is fatal. Magnetic resonance imaging (MRI) is one of the examination tools to examine brain anatomy in clinical practice. The high resolution and clear separation of the tissue enable medical experts to identify brain tumor. The earlier of brain tumor is detected, the wider of treatment options. However, manually analysed of brain anatomy on MRI images are time-consuming. Computer-aided diagnosis with automated way is helpful solution to help management with unreliable degrees of automation to trace various tissue boundaries. This study proposes convolutional neural network (CNN) with its excellences to automated features extraction in convolution layer. The popular architectures of CNN, i.e., visual geometry group16 (VGG16), residual network-50 (resNet-50), inceptionV3, mobileNet, and efficientNetB7 in medical image processing are compared to brain tumor classification task. As the results, VGG16 outperformed other architectures of CNN in this study. VGG16 yields 100% accuracy, precision, sensitivity, specificity, and F1-score for testing set data. The results show the excellent performance in classifying brain tumor and no tumor from MRI images that demonstrate the efficiency of system suggested.</p>

FCM and CBAC based Brain Tumor Identification and Segmentation

A brain tumor are an abnormal growth of cells within the brain, forming a mass that can be either cancerous (malignant) or non-cancerous (benign). Despite their differences, both types of tumors can pose serious health risks. As these tumors grow, they can increase intracranial pressure, leading to potential brain damage. This increased pressure can result in various symptoms such as headaches, seizures, vision problems, and changes in cognitive function. The potential for life-threatening consequences makes early detection and treatment crucial. The objective of the research is to develop a system or algorithm capable of accurately identifying the presence of brain tumors within medical imaging data (CT or MRI scans) and subsequently segmenting the tumor regions from the surrounding healthy brain tissue. This research aims at building an automated multi stage reliable system for classifying MRI images as tumor or non-tumor images. However, the research aims to diagnose brain tumor by extracting the tumor region accurately. The main contribution of this work is to automatically segment the tumor region from the MRI brain images, using Fuzzy C-Means (FCM) Clustering and the Content-Based Active Contour (CBAC) method. The CBAC method helps to resolve the issues of saddle points and broken edges in the extracted tumor region.

An effective ensemble learning approach for classification of glioma grades based on novel MRI features

The preoperative diagnosis of brain tumors is important for therapeutic planning as it contributes to the tumors’ prognosis. In the last few years, the development in the field of artificial intelligence and machine learning has contributed greatly to the medical area, especially the diagnosis of the grades of brain tumors through radiological images and magnetic resonance images. Due to the complexity of tumor descriptors in medical images, assessing the accurate grade of glioma is a major challenge for physicians. We have proposed a new classification system for glioma grading by integrating novel MRI features with an ensemble learning method, called Ensemble Learning based on Adaptive Power Mean Combiner (EL-APMC). We evaluate and compare the performance of the EL-APMC algorithm with twenty-one classifier models that represent state-of-the-art machine learning algorithms. Results show that the EL-APMC algorithm achieved the best performance in terms of classification accuracy (88.73%) and F1-score (93.12%) over the MRI Brain Tumor dataset called BRATS2015. In addition, we showed that the differences in classification results among twenty-two classifier models have statistical significance. We believe that the EL-APMC algorithm is an effective method for the classification in case of small-size datasets, which are common cases in medical fields. The proposed method provides an effective system for the classification of glioma with high reliability and accurate clinical findings.

Improving brain tumor classification with combined convolutional neural networks and transfer learning

Brain tumors pose a serious threat, causing the deaths of thousands of people worldwide, and can lead to life-threatening consequences when not accurately diagnosed. The classification of brain tumors and the determination of correct treatment strategies are crucial, yet this process encounters various challenges. These tumors originate from different cell types and exhibit diversity in growth rates, histological features, and genetic structures. Some may show similarities at the microscopic level, complicating classification and making diagnosis and treatment challenging. The examination of brain MR images is a widely used method in diagnosing brain tumors. However, occasional misdiagnosis of tumors can lead to ineffective responses to treatments and reduced chances of survival for patients. Traditional machine learning classifiers require manually determined features, which is quite time-consuming. On the other hand, deep learning is highly effective in feature extraction and has recently been widely preferred in classification. In this context, the effectiveness of transfer learning architectures in brain tumor diagnosis was evaluated. Six different transfer learning architectures, including ResNet-50, MobileNet, VGG16, Inception-V3, DenseNet-121, and EfficientNetV2-M, were used in this study. A public MRI dataset was used for model validation and comparison with similar studies. To address the imbalance in the number of images among classes in the dataset, data augmentation techniques such as random rotation were applied during data preprocessing. Experiments revealed that the EfficientNetV2-M model outperformed other architectures with a 98.01% accuracy rate. Additionally, the study aimed to create a new model with more comprehensive feature extraction and generalization ability by combining the advantages of multiple models in different combinations. In this context, combinations of EfficientNetV2-M architecture with Inception-V3 and DenseNet-121 architectures were formed. Through these combinations, a concatenation-based EfficientNetV2-M + Inception-V3 model achieved a 98.41% accuracy value. It was observed that the proposed concatenation-based model outperformed advanced methods in improving medical imaging techniques and patient outcomes.

Refining neural network algorithms for accurate brain tumor classification in MRI imagery

Brain tumor diagnosis using MRI scans poses significant challenges due to the complex nature of tumor appearances and variations. Traditional methods often require extensive manual intervention and are prone to human error, leading to misdiagnosis and delayed treatment. Current approaches primarily include manual examination by radiologists and conventional machine learning techniques. These methods rely heavily on feature extraction and classification algorithms, which may not capture the intricate patterns present in brain MRI images. Conventional techniques often suffer from limited accuracy and generalizability, mainly due to the high variability in tumor appearance and the subjective nature of manual interpretation. Additionally, traditional machine learning models may struggle with the high-dimensional data inherent in MRI images. To address these limitations, our research introduces a deep learning-based model utilizing convolutional neural networks (CNNs).Our model employs a sequential CNN architecture with multiple convolutional, max-pooling, and dropout layers, followed by dense layers for classification. The proposed model demonstrates a significant improvement in diagnostic accuracy, achieving an overall accuracy of 98% on the test dataset. The proposed model demonstrates a significant improvement in diagnostic accuracy, achieving an overall accuracy of 98% on the test dataset. The precision, recall, and F1-scores ranging from 97 to 98% with a roc-auc ranging from 99 to 100% for each tumor category further substantiate the model’s effectiveness. Additionally, the utilization of Grad-CAM visualizations provides insights into the model’s decision-making process, enhancing interpretability. This research addresses the pressing need for enhanced diagnostic accuracy in identifying brain tumors through MRI imaging, tackling challenges such as variability in tumor appearance and the need for rapid, reliable diagnostic tools.

Internet of things and deep learning based digital twins for diagnosis of brain tumor by analyzing MRI images

Although brain tumours are few, they have one of the highest mortality rates among all types of cancer due to their abnormal growth and proliferation. Brain tumours develop due to the accumulation of abnormal tissues in the brain. Various forms of abnormal tissue exist, however, in the majority of cases, they develop in a regular manner and perish without creating any detrimental effects. Digital twins are occasionally known as digital mirrors, digital mapping, and digital replicas. All of these are synonymous terms for the identical entity. It is a technique for transferring digital or physical information from one realm to another. Image processing involves enhancing or eliminating data from a photograph to achieve a certain objective. Convolutional neural networks are a specific type of neural network that take signals from images as input and produce the image itself or a subset of its elements as output. This research presents a technique for identifying brain cancers using digital replicas and advanced machine learning algorithms by analysing MRI images. Images obtained from MRI machines are stored in a centralised cloud using Internet of Things (IoT) digital devices. The input pictures and other health-related data are then retrieved from cloud storage. The Particle Swarm Optimization approach chooses features. Brain tumor images are classified using machine learning techniques such as convolutional neural networks, support vector machines, and extreme learning machines. The CNN algorithm demonstrates greater accuracy when assessing MRI images for the purpose of identifying brain tumours.

Enhancing EfficientNetv2 with global and efficient channel attention mechanisms for accurate MRI-Based brain tumor classification

The early and accurate diagnosis of brain tumors is critical for effective treatment planning, with Magnetic Resonance Imaging (MRI) serving as a key tool in the non-invasive examination of such conditions. Despite the advancements in Computer-Aided Diagnosis (CADx) systems powered by deep learning, the challenge of accurately classifying brain tumors from MRI scans persists due to the high variability of tumor appearances and the subtlety of early-stage manifestations. This work introduces a novel adaptation of the EfficientNetv2 architecture, enhanced with Global Attention Mechanism (GAM) and Efficient Channel Attention (ECA), aimed at overcoming these hurdles. This enhancement not only amplifies the model’s ability to focus on salient features within complex MRI images but also significantly improves the classification accuracy of brain tumors. Our approach distinguishes itself by meticulously integrating attention mechanisms that systematically enhance feature extraction, thereby achieving superior performance in detecting a broad spectrum of brain tumors. Demonstrated through extensive experiments on a large public dataset, our model achieves an exceptional high-test accuracy of 99.76%, setting a new benchmark in MRI-based brain tumor classification. Moreover, the incorporation of Grad-CAM visualization techniques sheds light on the model’s decision-making process, offering transparent and interpretable insights that are invaluable for clinical assessment. By addressing the limitations inherent in previous models, this study not only advances the field of medical imaging analysis but also highlights the pivotal role of attention mechanisms in enhancing the interpretability and accuracy of deep learning models for brain tumor diagnosis. This research sets the stage for advanced CADx systems, enhancing patient care and treatment outcomes.

CVG-Net: novel transfer learning based deep features for diagnosis of brain tumors using MRI scans

Brain tumors present a significant medical challenge, demanding accurate and timely diagnosis for effective treatment planning. These tumors disrupt normal brain functions in various ways, giving rise to a broad spectrum of physical, cognitive, and emotional challenges. The daily increase in mortality rates attributed to brain tumors underscores the urgency of this issue. In recent years, advanced medical imaging techniques, particularly magnetic resonance imaging (MRI), have emerged as indispensable tools for diagnosing brain tumors. Brain MRI scans provide high-resolution, non-invasive visualization of brain structures, facilitating the precise detection of abnormalities such as tumors. This study aims to propose an effective neural network approach for the timely diagnosis of brain tumors. Our experiments utilized a multi-class MRI image dataset comprising 21,672 images related to glioma tumors, meningioma tumors, and pituitary tumors. We introduced a novel neural network-based feature engineering approach, combining 2D convolutional neural network (2DCNN) and VGG16. The resulting 2DCNN-VGG16 network (CVG-Net) extracted spatial features from MRI images using 2DCNN and VGG16 without human intervention. The newly created hybrid feature set is then input into machine learning models to diagnose brain tumors. We have balanced the multi-class MRI image features data using the Synthetic Minority Over-sampling Technique (SMOTE) approach. Extensive research experiments demonstrate that utilizing the proposed CVG-Net, the k-neighbors classifier outperformed state-of-the-art studies with a k-fold accuracy performance score of 0.96. We also applied hyperparameter tuning to enhance performance for multi-class brain tumor diagnosis. Our novel proposed approach has the potential to revolutionize early brain tumor diagnosis, providing medical professionals with a cost-effective and timely diagnostic mechanism.

Integrated approach of federated learning with transfer learning for classification and diagnosis of brain tumor

Brain tumor classification using MRI images is a crucial yet challenging task in medical imaging. Accurate diagnosis is vital for effective treatment planning but is often hindered by the complex nature of tumor morphology and variations in imaging. Traditional methodologies primarily rely on manual interpretation of MRI images, supplemented by conventional machine learning techniques. These approaches often lack the robustness and scalability needed for precise and automated tumor classification. The major limitations include a high degree of manual intervention, potential for human error, limited ability to handle large datasets, and lack of generalizability to diverse tumor types and imaging conditions.To address these challenges, we propose a federated learning-based deep learning model that leverages the power of Convolutional Neural Networks (CNN) for automated and accurate brain tumor classification. This innovative approach not only emphasizes the use of a modified VGG16 architecture optimized for brain MRI images but also highlights the significance of federated learning and transfer learning in the medical imaging domain. Federated learning enables decentralized model training across multiple clients without compromising data privacy, addressing the critical need for confidentiality in medical data handling. This model architecture benefits from the transfer learning technique by utilizing a pre-trained CNN, which significantly enhances its ability to classify brain tumors accurately by leveraging knowledge gained from vast and diverse datasets.Our model is trained on a diverse dataset combining figshare, SARTAJ, and Br35H datasets, employing a federated learning approach for decentralized, privacy-preserving model training. The adoption of transfer learning further bolsters the model’s performance, making it adept at handling the intricate variations in MRI images associated with different types of brain tumors. The model demonstrates high precision (0.99 for glioma, 0.95 for meningioma, 1.00 for no tumor, and 0.98 for pituitary), recall, and F1-scores in classification, outperforming existing methods. The overall accuracy stands at 98%, showcasing the model’s efficacy in classifying various tumor types accurately, thus highlighting the transformative potential of federated learning and transfer learning in enhancing brain tumor classification using MRI images.

A Hybrid Technique to Predict Brain Tumour using MRI Image

Currently, the radiologist can more accurately identify brain tumours through the development of Computer-Assisted Diagnosis (CAD), Machine Learning and Deep Learning. Recently, Deep Learning (DL) strategies have gained traction as a means to rapidly and accurately construct automated systems for diagnosing and segmenting the image. The standard approach to this issue is to create a custom feature for classification. Most neurological diseases originate from abnormal growth of brain cells, which can compromise brain architecture and even lead to malignant brain tumours. Brain tumour detection and classification algorithms that are both quick and accurate have been the subject of extensive study. This facilitates the straight forward diagnosis of brain tumours using Magnetic Resonance Image (MRI) images. Through Deep Learning (DL) model the diagnosis of brain malignancies in MRI images using Convolutional Neural Network (CNN) is possible by training the data. So, in this paper the brain tumouris predicted byproposing a Hybridfeature extraction technique i.e., tuned CNN model with ResNet150 and U-net.

Genomic medicine advances for brain tumors.

Cancer genome profiling has revealed important genetic alterations that serve as prognostic indicators and guides for treatment decisions, enabling precision medicine. The shift to molecular diagnosis of brain tumors, as reflected in the 2021 World Health Organization Classification of Tumors of the Central Nervous System, is a crucial role for treatment decision-making. This review discusses the significance and role of cancer genome profiling in precision medicine for malignant brain tumors, particularly gliomas. Furthermore, we explore the progress in cancer genome analysis, focusing on cancer gene panel testing, integration of genomic information in brain tumor classification, and hereditary tumors. Additionally, we discuss the transformative effect of genomic medicine on early detection, risk assessment, and precision medicine strategies. The tumor mutational burden in brain tumors is considered low, but the application of molecular targeted drugs, such as isocitrate dehydrogenase inhibitors, v-raf murine sarcoma viral oncogene homolog B1 inhibitors, fibroblast growth factor receptor inhibitors, neurotrophic tyrosine receptor kinase, mechanistic target of rapamycin inhibitors, and anti-programmed death receptor-1 antibody drugs are promising for glioma treatment. We also discuss the future prospects of molecular targeted drugs.

A novel approach of brain-computer interfacing (BCI) and Grad-CAM based explainable artificial intelligence: Use case scenario for smart healthcare

BackgroundIn order to push the frontiers of brain-computer interface (BCI) and neuron-electronics, this research presents a novel framework that combines cutting-edge technologies for improved brain-related diagnostics in smart healthcare. This research offers a ground-breaking application of transparent strategies to BCI, promoting openness and confidence in brain-computer interactions and taking inspiration from Grad-CAM (Gradient-weighted Class Activation Mapping) based Explainable Artificial Intelligence (XAI) methodology. The landscape of healthcare diagnostics is about to be redefined by the integration of various technologies, especially when it comes to illnesses related to the brain. New methodA novel approach has been proposed in this study comprising of Xception architecture which is trained on imagenet database following transfer learning process for extraction of significant features from magnetic resonance imaging dataset acquired from publicly available distinct sources as an input and linear support vector machine has been used for distinguishing distinct classes.Afterwards, gradient-weighted class activation mapping has been deployed as the foundation for explainable artificial intelligence (XAI) for generating informative heatmaps, representing spatial localization of features which were focused to achieve model’s predictions. ResultsThus, the proposed model not only provides accurate outcomes but also provides transparency for the predictions generated by the Xception network to diagnose presence of abnormal tissues and avoids overfitting issues. Hyperparameters along with performance-metrics are also obtained while validating the proposed network on unseen brain MRI scans to ensure effectiveness of the proposed network. Comparison with existing methods and conclusionsThe integration of Grad-CAM based explainable artificial intelligence with deep neural network namely Xception offers a significant impact in diagnosing brain tumor disease while highlighting the specific regions of input brain MRI images responsible for making predictions. In this study, the proposed network results in 98.92% accuracy, 98.15% precision, 99.09% sensitivity, 98.18% specificity and 98.91% dice-coefficient while identifying presence of abnormal tissues in the brain. Thus, Xception model trained on distinct dataset following transfer learning process offers remarkable diagnostic accuracy and linear support vector act as a classifier to provide efficient classification among distinct classes. In addition, the deployed explainable artificial intelligence approach helps in revealing the reasoning behind predictions made by deep neural network having black-box nature and provides a clear perspective to assist medical experts in achieving trustworthiness and transparency while diagnosing brain tumor disease in the smart healthcare

Enhanced Brain Tumor Analysis: Integrating ResNet50 with Convolutional Block Attention Modules for Advanced Insights

The process of diagnosing brain tumors is a lengthy task that heavily relies on the experience of radiologists. However, deep learning techniques have become increasingly popular in automating the diagnosis of brain tumors, offering improved precision and effectiveness. One such technique, Convolutional Block Attention Modules (CBAM), uses attention-based models that dynamically enhance and refine diagnostic characteristics. However, the specific impact of using different attention methods, such as channel, spatial, or combined attention, within CBAM for brain tumor classification is yet to be fully explored.To address this gap, our research used ResNet50 coupled with CBAM to classify brain tumors. This novel approach demonstrated superior performance compared to existing methods, including Convolutional Neural Network. ResNet50-CBAM showed remarkable area under the curve (AUC), recall, precision, and accuracy of 99.53%, 99.11%, 98.75%, and 99.35%, respectively, using the same dataset.The fusion of ResNet-CBAM not only captures spatial context but also enhances feature representation, making it a promising integration into brain classification software platforms. This development could benefit doctors by improving brain tumor categorization and facilitating better clinical decision-making.