{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T01:44:52Z","timestamp":1782351892751,"version":"3.54.5"},"reference-count":43,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,29]],"date-time":"2021-12-29T00:00:00Z","timestamp":1640736000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003052","name":"Ministry of Trade, Industry and Energy","doi-asserted-by":"publisher","award":["20011875"],"award-info":[{"award-number":["20011875"]}],"id":[{"id":"10.13039\/501100003052","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Recently, the amount of attention paid towards convolutional neural networks (CNN) in medical image analysis has rapidly increased since they can analyze and classify images faster and more accurately than human abilities. As a result, CNNs are becoming more popular and play a role as a supplementary assistant for healthcare professionals. Using the CNN on portable medical devices can enable a handy and accurate disease diagnosis. Unfortunately, however, the CNNs require high-performance computing resources as they involve a significant amount of computation to process big data. Thus, they are limited to being used on portable medical devices with limited computing resources. This paper discusses the network quantization techniques that reduce the size of CNN models and enable fast CNN inference with an energy-efficient CNN accelerator integrated into recent mobile processors. With extensive experiments, we show that the quantization technique reduces inference time by 97% on the mobile system integrating a CNN acceleration engine.<\/jats:p>","DOI":"10.3390\/s22010219","type":"journal-article","created":{"date-parts":[[2021,12,29]],"date-time":"2021-12-29T08:12:15Z","timestamp":1640765535000},"page":"219","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["A Case Study of Quantizing Convolutional Neural Networks for Fast Disease Diagnosis on Portable Medical Devices"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-8704-8301","authenticated-orcid":false,"given":"Mukhammed","family":"Garifulla","sequence":"first","affiliation":[{"name":"School of Computer Science Engineering, Kyungpook National University, Daegu 41566, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juncheol","family":"Shin","sequence":"additional","affiliation":[{"name":"School of Computer Science Engineering, Kyungpook National University, Daegu 41566, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chanho","family":"Kim","sequence":"additional","affiliation":[{"name":"School of Computer Science Engineering, Kyungpook National University, Daegu 41566, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Won Hwa","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Radiology, Kyungpook National University Chilgok Hospital, Kyungpook National University, Daegu 41404, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0263-0941","authenticated-orcid":false,"given":"Hye Jung","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Radiology, Kyungpook National University Chilgok Hospital, Kyungpook National University, Daegu 41404, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9799-1773","authenticated-orcid":false,"given":"Jaeil","family":"Kim","sequence":"additional","affiliation":[{"name":"School of Computer Science Engineering, Kyungpook National University, Daegu 41566, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7842-125X","authenticated-orcid":false,"given":"Seokin","family":"Hong","sequence":"additional","affiliation":[{"name":"Department of Semiconductor Systems Engineering, Sungkyunkwan University, Suwon-si 16419, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,29]]},"reference":[{"key":"ref_1","first-page":"1284","article-title":"Rapid imaging, detection and quantification of Giardia lamblia cysts using mobile-phone based fluorescent microscopy and machine learning","volume":"15","author":"Koydemir","year":"2015","journal-title":"R. 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