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However, there is little research on the application of SPECT images in the computer-aided diagnosis of thyroid diseases based on machine learning methods. A convolutional neural network with optimization-based computer-aided diagnosis of thyroid diseases using SPECT images is developed. Three categories of diseases are considered, and they are Graves\u2019 disease, Hashimoto disease, and subacute thyroiditis. A modified DenseNet architecture of convolutional neural network is employed, and the training method is improved. The architecture is modified by adding the trainable weight parameters to each skip connection in DenseNet. And the training method is improved by optimizing the learning rate with flower pollination algorithm for network training. Experimental results demonstrate that the proposed method of convolutional neural network is efficient for the diagnosis of thyroid diseases with SPECT images, and it has superior performance than other CNN methods.<\/jats:p>","DOI":"10.1155\/2019\/6212759","type":"journal-article","created":{"date-parts":[[2019,1,15]],"date-time":"2019-01-15T18:31:54Z","timestamp":1547577114000},"page":"1-11","source":"Crossref","is-referenced-by-count":84,"title":["Thyroid Diagnosis from SPECT Images Using Convolutional Neural Network with Optimization"],"prefix":"10.1155","volume":"2019","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9515-7988","authenticated-orcid":true,"given":"Liyong","family":"Ma","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Harbin Institute of Technology, Weihai 264209, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengkuan","family":"Ma","sequence":"additional","affiliation":[{"name":"School of 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