Optimised Convolution Layers of DnCNN Using Vedic Multiplier and Hyperparameter Tuning in Cancer Detection on Field Programmable Gate Array.

IF 1.1 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
S Roobini Priya, Prema Vanaja Ranjan, Shanker Nagalingam Rajediran
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引用次数: 0

Abstract

Introduction: Recently, deep learning (DL) algorithms use Arithmetic Units (AU) in CPU/GPU hardware for processing images/data. AU operates in fixed precision and limits the representation of weights and activations in DL. The problem leads to quantization errors, which reduce accuracy during cancer cell segmentation.

Methods: In this study, arithmetic multiplication in convolution layers is replaced with Vedic multiplication in the proposed DnCNN algorithm. Next, Vedic multiplication-based convolution layers in the DnCNN architecture are optimized using POA (Pelican Optimization Algorithm), and the resulting POA-DnCNN is implemented on an FPGA device for breast cancer detection, segmentation, and classification of benign and malignant breast lesions.

Discussion: In the convolution layer of DnCNN, floating-point operations are performed through the Hybrid-Vedic (HV) multiplier called 'CUTIN,' which is the combination of Urdhva Tryambakam and Nikhilam Sutra with the upasutra 'Anurupyena.' Larger image sizes increase processor size and gate count.

Results: The proposed HV-FPGA-based breast cancer detection system, employing Vedic multiplication in the convolution layers of DnCNN and hyperparameters optimized by POA, detects stages of breast cancer with an accuracy of 96.3%, precision of 94.54%, specificity of 92.37%, F-score of 93.56%, IoU of 94.78%, and DSC of 95.45%, outperforming existing methods.

Conclusion: The proposed CUTIN multiplier uses a CSA (carry save adder) with simplified sum-carry generation logic (CSCGL), achieving lower area-delay, high speed, and improved precision.

基于Vedic乘法器和超参数调优的DnCNN卷积层在现场可编程门阵列癌症检测中的应用。
最近,深度学习(DL)算法使用CPU/GPU硬件中的算术单元(AU)来处理图像/数据。AU以固定精度操作,并且限制了DL中权重和激活的表示。这个问题导致量化误差,降低了癌细胞分割的准确性。方法:在本文提出的DnCNN算法中,将卷积层中的算术乘法替换为吠陀乘法。接下来,利用POA (Pelican Optimization Algorithm)对DnCNN架构中基于Vedic乘法的卷积层进行优化,并将得到的POA-DnCNN在FPGA器件上实现,用于乳腺癌检测、分割以及乳腺良恶性病变的分类。讨论:在DnCNN的卷积层中,浮点运算是通过称为“CUTIN”的混合吠陀(HV)乘法器执行的,该乘法器是Urdhva Tryambakam和Nikhilam经与upasutra“Anurupyena”的组合。较大的图像尺寸增加了处理器尺寸和门数。结果:基于hv - fpga的乳腺癌检测系统,采用DnCNN卷积层的Vedic乘法和POA优化的超参数,准确率为96.3%,精密度为94.54%,特异性为92.37%,f评分为93.56%,IoU为94.78%,DSC为95.45%,优于现有方法。结论:所提出的CUTIN乘法器采用简化和进位生成逻辑(CSCGL)的CSA(进位节省加法器),具有面积延迟小、速度快、精度高的优点。
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来源期刊
CiteScore
2.60
自引率
0.00%
发文量
246
审稿时长
1 months
期刊介绍: Current Medical Imaging Reviews publishes frontier review articles, original research articles, drug clinical trial studies and guest edited thematic issues on all the latest advances on medical imaging dedicated to clinical research. All relevant areas are covered by the journal, including advances in the diagnosis, instrumentation and therapeutic applications related to all modern medical imaging techniques. The journal is essential reading for all clinicians and researchers involved in medical imaging and diagnosis.
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