基于MRI的脑肿瘤检测与定位的高效特征提取与分类架构
Efficient Feature Extraction and Classification Architecture for MRI-Based Brain Tumor Detection and Localization
AI总结：
本文提出一种基于MRI和CNN的高效脑肿瘤检测与定位架构，准确率达99.17%，并结合GradCAM定位肿瘤区域及多机器学习模型评估，辅助医生提升诊断准确性。
摘要：
大脑中不受控制的细胞分裂是脑肿瘤产生的原因。如果肿瘤体积增大超过一半，患者康复的希望就十分渺茫。这凸显了对脑肿瘤进行快速且精确诊断的必要性。在分析、诊断和规划脑肿瘤治疗方案时，MRI成像发挥着至关重要的作用。脑肿瘤的发展历史是医生需要掌握的关键信息。在区分人体软组织方面，MRI扫描具有优势。为了快速从MRI扫描中获得可靠的分类结果，深度学习是最实用的方法之一。已有研究表明，采用深度学习方法能够提高早期人类疾病诊断的准确性。在脑肿瘤诊断中，即使是轻微的误诊也可能带来严重后果，因此准确性尤为重要。在医学图像中揭示脑肿瘤仍然是一项困难的任务。脑部MRI在显示肿瘤存在与否方面以不精确而著称。本研究利用脑部MRI扫描训练了一个CNN，用于识别肿瘤的存在。CNN模型的结果显示准确率达到99.17%。同时，我们还提取了CNN模型的特征。此外，我们还使用深度学习可解释性工具GradCAM从未标注图像中定位了肿瘤区域。为了评估该CNN模型处理图像的能力，我们将这些特征输入到不同的机器学习模型中。CNN和机器学习模型还使用精确率、召回率、特异性和F1分数等标准指标进行了评估。医生诊断的重要性提升了CNN模型在辅助识别肿瘤存在和治疗患者方面的准确性。
英文摘要：
Uncontrolled cell division in the brain is what gives rise to brain tumors. If the tumor size increases by more than half, there is little hope for the patient's recovery. This emphasizes the need of rapid and precise brain tumor diagnosis. When it comes to analyzing, diagnosing, and planning therapy for brain tumors, MRI imaging plays a crucial role. A brain tumor's development history is crucial information for doctors to have. When it comes to distinguishing between human soft tissues, MRI scans are superior. In order to get reliable classification results from MRI scans quickly, deep learning is one of the most practical methods. Early human illness diagnosis has been demonstrated to be more accurate when deep learning methods are used. In the case of diagnosing a brain tumor, when even a little misdiagnosis might have serious consequences, accuracy is especially important. Disclosure of brain tumors in medical images is still a difficult task. Brain MRIs are notoriously imprecise in revealing the presence or absence of tumors. Using MRI scans of the brain, a CNN was trained to identify the presence of a tumor in this research. Results from the CNN model showed an accuracy of 99.17%. The CNN model's characteristics were also retrieved. The CNN model's characteristics were also retrieved and we also localized the tumor regions from the unannotated images using GradCAM, a deep learning explainability tool. In order to evaluate the CNN model's capability for processing images, we applied the features into different ML models. CNN and machine learning models were also evaluated using the standard metrics of Precision, Recall, Specificity, and F1 score. The significance of the doctor's diagnosis enhanced the accuracy of the CNN model's assistance in identifying the existence of tumor and treating the patient.
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