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Căutarea după subiecte similare conform CZU |
004.032.26:616.24-006 (1) |
Computer science and technology. Computing. Data processing (4156) |
Pathology of the respiratory system. Complaints of the respiratory organs (757) |
SM ISO690:2012 MATHEWS, Arun B., PRASAD, Krishna K.. A novel classification with deep convolutional neural networks on pulmonary nodule. In: Journal of Engineering Sciences, 2022, vol. 29, nr. 3, pp. 86-92. ISSN 2587-3474. DOI: https://doi.org/10.52326/jes.utm.2022.29(3).08 |
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Journal of Engineering Sciences | ||||||
Volumul 29, Numărul 3 / 2022 / ISSN 2587-3474 /ISSNe 2587-3482 | ||||||
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DOI:https://doi.org/10.52326/jes.utm.2022.29(3).08 | ||||||
CZU: 004.032.26:616.24-006 | ||||||
Pag. 86-92 | ||||||
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Descarcă PDF | ||||||
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Medical images are an important part of a patient's health record, and they need data manipulation, processing, and handling by computers. As a result, medical data is a type of bigdata, and its analysis has become complex. Because manual disease diagnosis takes longer and produces less accurate results, it may result in incorrect treatment. Three DCNN architectures have been exploited and evaluated for tumor detection and classification. The sample image for the experimentation is chosen from Lung Image Database Consortium (LIDC) with Image Database Resource Initiative (IDRI) and Kaggle dataset which consists of normal and abnormal image. The experimental results of proposed DCNN classifier achieved best accuracy than the GoogleNet, AlexNet, Artificial neural network and support vector machine. |
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Cuvinte-cheie lung cancer, DCNN, LIDC and GoogleNet, AlexNet, cancer pulmonar, DCNN, LIDC și GoogleNet, AlexNet |
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