Abstract
Brain cancer represents a major challenge in medical diagnostics, requisite precise and timely detection for effective treatment. Diagnosis initially relies on the proficiency of radiologists, which can cause difficulties and threats when the expertise is sparse. Despite the use of imaging resources, brain cancer remains often difficult, time-consuming, and vulnerable to intraclass variability. This study conveys the Bangladesh Brain Cancer MRI Dataset, containing 6,056 MRI images organized into three categories: Brain Tumor, Brain Glioma, and Brain Menin. The dataset was collected from several hospitals in Bangladesh, providing a diverse and realistic sample for research. We implemented advanced deep learning models, and DenseNet169 achieved exceptional results, with accuracy, precision, recall, and F1-Score all reaching 0.9983. In addition, Explainable AI (XAI) methods including GradCAM, GradCAM++, ScoreCAM, and LayerCAM were employed to provide visual representations of the decision-making processes of the models. In the context of brain cancer, these techniques highlight DenseNet169's potential to enhance diagnostic accuracy while simultaneously offering transparency, facilitating early diagnosis and better patient outcomes.
Abstract (translated)
脑癌在医学诊断中是一个重大挑战,需要精准和及时的检测以进行有效的治疗。最初的诊断依赖于放射科医生的专业技能,但当专家资源稀少时,这会带来困难甚至威胁。尽管使用了成像资源,脑癌仍然常常难以诊断、耗时且容易受到类内变异性的困扰。本研究介绍了孟加拉国脑癌MRI数据集,该数据集中包含6,056张MRI图像,并按三个类别进行组织:脑肿瘤、脑胶质瘤和脑膜瘤。这些数据是从孟加拉国的多家医院收集的,为研究提供了多样且现实的样本。 我们实施了先进的深度学习模型,其中DenseNet169取得了卓越的结果,其准确率、精确度、召回率以及F1分数均达到了0.9983。此外,我们还应用了解释性人工智能(XAI)方法,包括GradCAM、GradCAM++、ScoreCAM和LayerCAM等,用以提供模型决策过程的可视化表示。 在脑癌诊断领域中,这些技术突显了DenseNet169增强诊断准确性的同时提供了透明度,有助于早期诊断及改善患者的治疗效果。
URL
https://arxiv.org/abs/2501.05426