腦腫瘤是最致命的癌症之一，如果利用自動化腦瘤檢測系統來輔助醫生就能使流程更加快速，也能和人工檢測的結果做雙重確認。近年來卷積神經網路（Convolutional Neural Network, CNN）已在腦瘤檢測方面取得進展，但運算量複雜且參數量大仍是挑戰。因此本研究創建一個輕量化的腦瘤檢測模型，優化方式包括Fire module、全局平均池化及資料增強等。另外本論文提出一個基於硬體的演算法簡化，針對有批次正規化的CNN架構做計算及參數合併，能在同樣準確率下減少硬體複雜度。
最後本論文會探討腦瘤檢測系統的超大型積體電路(Very Large Scale Integration ,VLSI)設計與實作，並以降低面積及縮短延遲(Latency)為設計方向。實驗結果顯示，和AlexNet架構比較之下，本論文的架構準確率在浮點數運算降低了1.07%、定點數降低了1.23%，且GOP/s只有其1/65，但硬體使用量降低了許多，相對地此模型的泛化能力比AlexNet差，因為此模型僅限於腦瘤檢測。如果同時考慮面積及速度，Area efficiency相較2018年文獻提升了20.3%，能使用更小的面積嵌入MRI儀器中。

Brain tumors are among the most lethal types of cancer. Using automated brain tumor detection systems to assist doctors can expedite the process and provide dual confirmation with manual detection results. In recent years, Convolutional Neural Networks (CNNs) have made progress in brain tumor detection, but the computational complexity and large number of parameters pose challenges. This study creates a lightweight brain tumor detection model. Moreover, this paper proposes a hardware-based algorithm simplification that targets the calculation and parameter combination of CNN structures with batch normalization, reducing hardware complexity while maintaining the same accuracy level.
Lastly, this paper explores the design and implementation of the brain tumor detection system using Very Large Scale Integration (VLSI), circuits with the design direction focusing on reducing area and shortening latency. Experimental results show that compared with the AlexNet architecture, the accuracy of the proposed architecture decreased by 1.07%, but the hardware resource usage has decreased significantly. Conversely, the generalization ability of this model is inferior to AlexNet, as this model is solely designed for brain tumor detection. When considering both area and speed, the area efficiency increased by 20% compared to the literature in 2018.

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