Introduction: The early identification of brain tumors is essential for optimal treatment and patient prognosis. Advancements in MRI technology have markedly enhanced tumor detection yet necessitate accurate classification for appropriate therapeutic approaches. This underscores the necessity for sophisticated diagnostic instruments that are precise and comprehensible to healthcare practitioners.Methods: Our research presents CNN-TumorNet, a convolutional neural network for categorizing MRI images into tumor and non-tumor categories. Although deep learning models exhibit great accuracy, their complexity frequently restricts clinical application due to inadequate interpretability. To address this, we employed the LIME technique, augmenting model transparency and offering explicit insights into its decision-making process.Results: CNN-TumorNet attained a 99% accuracy rate in differentiating tumors from non-tumor MRI scans, underscoring its reliability and efficacy as a diagnostic instrument. Incorporating LIME guarantees that the model’s judgments are comprehensible, enhancing its clinical adoption.Discussion: Despite the efficacy of CNN-TumorNet, the overarching challenge of deep learning interpretability persists. These models may function as ”black boxes,” complicating doctors’ ability to trust and accept them without comprehending their rationale. By integrating LIME, CNN-TumorNet achieves elevated accuracy alongside enhanced transparency, facilitating its application in clinical environments and improving patient care in neuro-oncology.

Accurate segmentation of brain tumors from MRI (Magnetic Resonance Imaging) sequences is essential across diverse clinical scenarios, facilitating precise delineation of anatomical structures and disease-affected areas. This study presents an innovative deep-learning method for segmenting glioma brain tumors, utilizing a hybrid architecture that combines ResNet U-Net with Transformer blocks. The proposed model adeptly encompasses both the local and global contextual details present in MRI scans. The architecture includes an encoder based on ResNet for extracting hierarchical features, followed by residual blocks to enhance feature representation while maintaining spatial information. Additionally, a central transformer block, incorporating multi-head attention mechanisms, enables the modeling of long-range dependencies and contextual comprehension, progressively refining feature interactions. To handle structural scale variations within MRI images, skip connections are utilized during the decoding phase. Transposed convolutional layers in the decoder upsample feature maps, retaining details and incorporating contextual information from earlier layers. A rigorous assessment of the model’s functionality was carried out with the BraTS2019 dataset, employing a comprehensive set of evaluation metrics including accuracy, IOU score, specificity, sensitivity, dice score, and precision. The evaluation focused on individual tumor classes, namely the whole, core, and enhancing tumor regions. During validation, the suggested model demonstrated remarkable dice scores of 0.91, 0.89, and 0.84 for the whole tumor, core tumor, and enhancing tumor, respectively, yielding an impressive overall accuracy rate of 98%.

Artificial Intelligence (AI) and Machine Learning (ML) play a crucial role in developing disruptive healthcare technologies since they address the needs of consumers by providing precise and effective diagnostic and decision-making capabilities. The influence of these factors on diagnosing and making decisions is especially noteworthy when collecting valuable information from healthcare data. This work offers “AC” (Accurate Cardiac Classification), a hybrid deep learning model that correctly detects cardiovascular disease (CVD) and provides meaningful insights. Convolutional neural networks and the Light Gradient Boosting Model integrate well in the “AC” model, simplifying feature learning and predictions. Using the CDC’s Behavioral Risk Factor Surveillance System (BRFSS) dataset, the proposed method outperforms current algorithms in accuracy, precision, recall, and F1 score. This dataset collects reliable, state-specific data on preventive health practices and risk behaviors related to chronic illnesses, injuries, and avoidable infections in adults, catering to the information needs of healthcare consumers. The “AC” model’s outstanding forecast accuracy is further improved by integrating an explainable AI methodology, specifically emphasizing SHAP’s local and global explanations. These explanatory insights clarify the model’s decision-making process. With healthcare technology’s advancement and the requirement for efficient data analysis to gather information, the “AC” model’s accuracy and comprehensibility may substantially improve healthcare professionals’ diagnosis and treatment skills, benefiting healthcare consumers.

Brain tumors provide a significant healthcare concern worldwide due to their potentially lethal consequences and the intricate nature of their diagnosis. These tumors exhibit significant variability in kind, size, and location, hence confounding identification and treatment efforts. Timely and precise detection is essential, as it profoundly influences treatment efficacy and survival probabilities. Contemporary diagnostic techniques, predominantly reliant on Magnetic Resonance Imaging (MRI), necessitate considerable manual analysis by experts, resulting in possible delays and inconsistencies in diagnosis. In response to the urgency of these difficulties, our research presents a novel multi-task learning methodology utilizing advanced neural network architectures to automate and improve the accuracy of brain tumor identification and classification from MRIs. This method seeks to optimize the diagnostic procedure, diminish reliance on manual analysis, and deliver swift, dependable outcomes that can expedite the commencement of treatment. The efficacy of our methodology is evidenced by comprehensive testing of three sophisticated neural architectures: UNet, Attention-UNet, and Residual-Attention-UNet. Our results indicate that the Residual-Attention-UNet model significantly surpasses the others in segmentation accuracy and classification precision. Our studies, utilizing conventional metrics including the Jaccard Similarity Index (JSI), Dice Coefficients (DC), and overall Accuracy (ACC), demonstrated that the Residual-Attention-UNet attained roughly 89.30% JSI, 91.10% DC, and 93.35% ACC. In binary classification tasks, this model achieved a Precision of 98.60%, Recall of 98.06%, Accuracy of 99.40%, and an F1 score of 96.57%. Furthermore, in multiclass classification contexts, the model consistently above 95% across all measures, underscoring its robustness and the efficacy of our proposed multi-task learning approach. These results highlight the capability of our method to substantially progress the domain of medical imaging for brain tumors, providing a robust instrument for improving diagnostic precision and patient management in neuro-oncology.