Abstract

The early diagnosis of brain tumors is critical in the area of healthcare, owing to the potentially life-threatening repercussions unstable growths within the brain can pose to individuals. The accurate and early diagnosis of brain tumors enables prompt medical intervention. In this context, we have established a new model called MTAP to enable a highly accurate diagnosis of brain tumors. The MTAP model addresses dataset class imbalance by utilizing the ADASYN method, employs a network pruning technique to reduce unnecessary weights and nodes in the neural network, and incorporates Avg-TopK pooling method for enhanced feature extraction. The primary goal of our research is to enhance the accuracy of brain tumor type detection, a critical aspect of medical imaging and diagnostics. The MTAP model introduces a novel classification strategy for brain tumors, leveraging the strength of deep learning methods and novel model refinement techniques. Following comprehensive experimental studies and meticulous design, the MTAP model has achieved a state-of-the-art accuracy of 99.69%. Our findings indicate that the use of deep learning and innovative model refinement techniques shows promise in facilitating the early detection of brain tumors. Analysis of the model’s heat map revealed a notable focus on regions encompassing the parietal and temporal lobes.

Graphical Abstract

Grad-CAM heat map visualization results

中文翻译：

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通过 MTAP 模型推进脑肿瘤分类：医学诊断的创新方法

摘要

脑肿瘤的早期诊断在医疗保健领域至关重要，因为大脑内不稳定的生长可能对个人造成潜在的危及生命的影响。脑肿瘤的准确和早期诊断可以实现及时的医疗干预。在此背景下，我们建立了一种称为 MTAP 的新模型，以实现脑肿瘤的高度准确诊断。 MTAP模型利用ADASYN方法解决数据集类别不平衡问题，采用网络剪枝技术来减少神经网络中不必要的权重和节点，并结合Avg-TopK池化方法来增强特征提取。我们研究的主要目标是提高脑肿瘤类型检测的准确性，这是医学成像和诊​​断的一个关键方面。 MTAP 模型利用深度学习方法和新颖的模型细化技术的优势，引入了一种新颖的脑肿瘤分类策略。经过全面的实验研究和精心设计，MTAP模型的准确率达到了99.69%。我们的研究结果表明，深度学习和创新模型细化技术的使用在促进脑肿瘤的早期检测方面显示出希望。对模型热图的分析表明，重点关注包括顶叶和颞叶的区域。

图形概要

Grad-CAM 热图可视化结果