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芯片带有涂覆胶塑封器件超声扫描检测方法研究
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作者 杨发明 《电子与封装》 2018年第A01期6-8,共3页
对于常规塑封器件,超声扫描检测可以快速、便捷地发现器件中的分层、裂缝、空洞、粘接层等典型缺陷,但是随着器件制造业的不断发展,许多新型器件、新的封装、新工艺不断涌现,给超声扫描检测试验引入新的难题。针对芯片带有涂覆胶的... 对于常规塑封器件,超声扫描检测可以快速、便捷地发现器件中的分层、裂缝、空洞、粘接层等典型缺陷,但是随着器件制造业的不断发展,许多新型器件、新的封装、新工艺不断涌现,给超声扫描检测试验引入新的难题。针对芯片带有涂覆胶的这种工艺,在进行超声扫描检测时就很容易被芯片表面涂覆胶所产生的异常波形所误导,会将正常的合格器件误判为不合格。提出了一种声扫、X光、剖面检测相结合的综合检测方式,有效解决了该类问题。 展开更多
关键词 超声扫描检测 涂覆胶 剖面检查
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Automatic anatomical classification of colonoscopic images using deep convolutional neural networks
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作者 Hiroaki Saito Tetsuya Tanimoto +7 位作者 Tsuyoshi Ozawa Soichiro Ishihara Mitsuhiro Fujishiro Satoki Shichijo Dai Hirasawa Tomoki Matsuda Yuma Endo Tomohiro Tada 《Gastroenterology Report》 SCIE EI 2021年第3期226-233,I0002,共9页
Background:A colonoscopy can detect colorectal diseases,including cancers,polyps,and inflammatory bowel diseases.A computer-aided diagnosis(CAD)system using deep convolutional neural networks(CNNs)that can recognize a... Background:A colonoscopy can detect colorectal diseases,including cancers,polyps,and inflammatory bowel diseases.A computer-aided diagnosis(CAD)system using deep convolutional neural networks(CNNs)that can recognize anatomical locations during a colonoscopy could efficiently assist practitioners.We aimed to construct a CAD system using a CNN to distinguish colorectal images from parts of the cecum,ascending colon,transverse colon,descending colon,sigmoid colon,and rectum.Method:We constructed a CNN by training of 9,995 colonoscopy images and tested its performance by 5,121 independent colonoscopy images that were categorized according to seven anatomical locations:the terminal ileum,the cecum,ascending colon to transverse colon,descending colon to sigmoid colon,the rectum,the anus,and indistinguishable parts.We examined images taken during total colonoscopy performed between January 2017 and November 2017 at a single center.We evaluated the concordance between the diagnosis by endoscopists and those by the CNN.The main outcomes of the study were the sensitivity and specificity of the CNN for the anatomical categorization of colonoscopy images.Results:The constructed CNN recognized anatomical locations of colonoscopy images with the following areas under the curves:0.979 for the terminal ileum;0.940 for the cecum;0.875 for ascending colon to transverse colon;0.846 for descending colon to sigmoid colon;0.835 for the rectum;and 0.992 for the anus.During the test process,the CNN system correctly recognized 66.6%of images.Conclusion:We constructed the new CNN system with clinically relevant performance for recognizing anatomical locations of colonoscopy images,which is the first step in constructing a CAD system that will support us during colonoscopy and provide an assurance of the quality of the colonoscopy procedure. 展开更多
关键词 COLONOSCOPY deep learning ENDOSCOPY neural network
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