A review on lung carcinoma segmentation and classification using CT image based on deep learning

Kanchana, M.; Poonkodi, S.

doi:10.1504/ijista.2022.10050327

Cited by 1 publication

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“…In CT images, the correlation between animal tissues and X-rays is reflected by Hounsfeld Units (HU) values, and tissues proportions are quantified in different gray levels [4]. Due to the fact that CT imaging technology can classify lesions and other abnormal areas of the scanned object, it is helpful in quickly and accurately diagnosing diseases in medical applications [5].…”

Section: Introductionmentioning

confidence: 99%

CT image segmentation of meat sheep Loin based on deep learning

Cao,

Lu,

Yang

et al. 2023

PLoS ONE

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There are no clear boundaries between internal tissues in sheep Computerized Tomography images, and it is difficult for traditional methods to meet the requirements of image segmentation in application. Deep learning has shown excellent performance in image analysis. In this context, we investigated the Loin CT image segmentation of sheep based on deep learning models. The Fully Convolutional Neural Network (FCN) and 5 different UNet models were applied in image segmentation on the data set of 1471 CT images including the Loin part from 25 Australian White rams and Dolper rams using the method of 5-fold cross validation. After 10 independent runs, different evaluation metrics were applied to assess the performances of the models. All models showed excellent results in terms evaluation metrics. There were slight differences among the results from the six models, and Attention-UNet outperformed others methods with 0.998±0.009 in accuracy, 4.391±0.338 in AVER_HD, 0.90±0.012 in MIOU and 0.95±0.007 in DICE, respectively, while the optimal value of LOSS was 0.029±0.018 from Channel-UNet, and the running time of ResNet34-UNet is the shortest.

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Section: Introductionmentioning

confidence: 99%