@inproceedings{oai:ipsj.ixsq.nii.ac.jp:00229903,
 author = {ホンズオン, グェン},
 book = {第85回全国大会講演論文集},
 issue = {1},
 month = {Feb},
 note = {Early diagnosis of thorax diseases may improve a patient's chances of cure and recovery. Recently, deep learning approaches are applied to multilabel classification of chest X-ray images. However, multilabel causes imbalance in the train data is a problem that happens with a variety of data, especially health data. This study aims to improve the performance of diseases detection from X-ray images. After adjusting the balance of data sample among different disease labels, a voting classification method has been involved to combine the results from different models. As a result, a meaningful improvement has been achieved.},
 pages = {209--210},
 publisher = {情報処理学会},
 title = {複数学習モデルにvoting分類を用いた胸部X線画像から疾患のマルチラベル診断について},
 volume = {2023},
 year = {2023}
}