Brain tumors pose a singularly formidable threat in contemporary healthcare due to their diverse histological profiles and unpredictable clinical behavior.Their spectrum ranges from slow-growing benign tumors to highl...Brain tumors pose a singularly formidable threat in contemporary healthcare due to their diverse histological profiles and unpredictable clinical behavior.Their spectrum ranges from slow-growing benign tumors to highly aggressive malignancies in sensitive anatomical locations.This necessitates an intensified focus on their path-ophysiology and demands precise characterization for patient-specific therapeutic solutions.Techniques to correctly identify brain tumors using artificial intelligence are often employed for addressing segmentation and detection tasks;however,the lack of generalizable results hinders medical practitioners from incorporating them into the diagnostic process.Predominantly reliant on Magnetic Resonance Imaging,research on other imaging methods like Positron Emission Tomography&Computed Tomography,is scarce due to a dearth of open-access datasets.Our study proposes a robust MBTC-Net framework by leveraging EfficientNetV2B0 for extracting high-dimensional feature maps,followed by reshaping into sequences and applying multi-head attention to capture contextual dependencies.After reintroducing the attention output into a spatial structure,we perform average pooling before transitioning to dense layers,enhanced with batch normalization and dropout.The model is fine-tuned with the Adamax optimizer to classify various kinds of brain tumors using softmax from T1-weighted,T1 Contrast-Enhanced,&T2-weighted MRI sequences and CT scans.To reduce the risk of overfitting,measures such as stratified 5-fold cross-validation have been extensively implemented across 3 open-access Kaggle datasets,obtaining 97.54%(15-class),97.97%(6-class),and 99.34%(2-class)accuracies,respectively.We have also applied Grad-CAM to decipher and visually analyze the predictions made by this framework.This research underscores the need for multimodal training of CT scans and MRI sequences for deploying a sturdy framework in real-time environments and advancing the well-being of patients.展开更多
文摘Brain tumors pose a singularly formidable threat in contemporary healthcare due to their diverse histological profiles and unpredictable clinical behavior.Their spectrum ranges from slow-growing benign tumors to highly aggressive malignancies in sensitive anatomical locations.This necessitates an intensified focus on their path-ophysiology and demands precise characterization for patient-specific therapeutic solutions.Techniques to correctly identify brain tumors using artificial intelligence are often employed for addressing segmentation and detection tasks;however,the lack of generalizable results hinders medical practitioners from incorporating them into the diagnostic process.Predominantly reliant on Magnetic Resonance Imaging,research on other imaging methods like Positron Emission Tomography&Computed Tomography,is scarce due to a dearth of open-access datasets.Our study proposes a robust MBTC-Net framework by leveraging EfficientNetV2B0 for extracting high-dimensional feature maps,followed by reshaping into sequences and applying multi-head attention to capture contextual dependencies.After reintroducing the attention output into a spatial structure,we perform average pooling before transitioning to dense layers,enhanced with batch normalization and dropout.The model is fine-tuned with the Adamax optimizer to classify various kinds of brain tumors using softmax from T1-weighted,T1 Contrast-Enhanced,&T2-weighted MRI sequences and CT scans.To reduce the risk of overfitting,measures such as stratified 5-fold cross-validation have been extensively implemented across 3 open-access Kaggle datasets,obtaining 97.54%(15-class),97.97%(6-class),and 99.34%(2-class)accuracies,respectively.We have also applied Grad-CAM to decipher and visually analyze the predictions made by this framework.This research underscores the need for multimodal training of CT scans and MRI sequences for deploying a sturdy framework in real-time environments and advancing the well-being of patients.
