東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Augmented Dual Input CNN (DI-CNN) fo...

Jain, Arshita.

FindBook

Google Book

Amazon

博客來

Augmented Dual Input CNN (DI-CNN) for the Diagnostic Classification of Lung Nodule Malignancy from CT Scans.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Augmented Dual Input CNN (DI-CNN) for the Diagnostic Classification of Lung Nodule Malignancy from CT Scans./
作者:	Jain, Arshita.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	60 p.
附註:	Source: Masters Abstracts International, Volume: 82-05.
Contained By:	Masters Abstracts International82-05.
標題:	Computer science. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28028937
ISBN:	9798678176745

Augmented Dual Input CNN (DI-CNN) for the Diagnostic Classification of Lung Nodule Malignancy from CT Scans.
Jain, Arshita.

Augmented Dual Input CNN (DI-CNN) for the Diagnostic Classification of Lung Nodule Malignancy from CT Scans. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 60 p.

Source: Masters Abstracts International, Volume: 82-05.

Thesis (M.S.)--University of Maryland, Baltimore County, 2020.

This item must not be sold to any third party vendors.

We demonstrate that Image Augmentation with a Dual-Input CNN architecture can greatly improve the diagnostic classification performance of a model for AI-based lung nodule malignancy classification from CT scan images. Lung cancer is the leading cause of cancer-related death worldwide, but early detection can improve prognosis. Lung cancer screening using Low Dose Computed Tomography (LDCT) has become a standard practice as a way of determining which pulmonary nodules are likely benign and which nodules require biopsy to determine malignancy. In recent years many studies have investigated the use of CNNs for malignancy estimation using LIDC-IDRI. Recent progress has shown that hybrid algorithms that combine CNNs with Radiomic features can achieve a high accuracy. Furthermore, additional studies have shown that Multi-Path CNN (MP-CNN) architectures are more accurate for this task than CNN architectures using a single input path. We present a novel approach using a special case of the MP-CNN in which both inputs are of the same dimensions, for which we coin Dual Input - CNN (DI-CNN). Furthermore, we greatly increase the labeled data volume of the LIDC-IDRI by incorporating rotation-based augmentation. We observe that the DI-CNN is the most accurate version of MP-CNN of several input dimensions in our comparison, thereby demonstrating that it is not necessary for both input paths to take imagery of different dimensions as was previously thought. Furthermore, we show that through data augmentation, it is possible to substantially increase the labeled data volume thereby allowing the DI-CNN algorithm to outperform a state-of-the-art hybrid CNN/Radiomic algorithm for classification of nodule malignancy.

ISBN: 9798678176745Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Dual Input CNN

Augmented Dual Input CNN (DI-CNN) for the Diagnostic Classification of Lung Nodule Malignancy from CT Scans.
LDR:02958nmm a2200385 4500 001 2281256
005 20210910100653.5
008 220723s2020 ||||||||||||||||| ||eng d
020 $a 9798678176745
035 $a (MiAaPQ)AAI28028937
035 $a AAI28028937
040 $a MiAaPQ $c MiAaPQ
100 1 $a Jain, Arshita. $3 3559840
245 1 0 $a Augmented Dual Input CNN (DI-CNN) for the Diagnostic Classification of Lung Nodule Malignancy from CT Scans.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 60 p.
500 $a Source: Masters Abstracts International, Volume: 82-05.
500 $a Advisor: Chapman, David.
502 $a Thesis (M.S.)--University of Maryland, Baltimore County, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a We demonstrate that Image Augmentation with a Dual-Input CNN architecture can greatly improve the diagnostic classification performance of a model for AI-based lung nodule malignancy classification from CT scan images. Lung cancer is the leading cause of cancer-related death worldwide, but early detection can improve prognosis. Lung cancer screening using Low Dose Computed Tomography (LDCT) has become a standard practice as a way of determining which pulmonary nodules are likely benign and which nodules require biopsy to determine malignancy. In recent years many studies have investigated the use of CNNs for malignancy estimation using LIDC-IDRI. Recent progress has shown that hybrid algorithms that combine CNNs with Radiomic features can achieve a high accuracy. Furthermore, additional studies have shown that Multi-Path CNN (MP-CNN) architectures are more accurate for this task than CNN architectures using a single input path. We present a novel approach using a special case of the MP-CNN in which both inputs are of the same dimensions, for which we coin Dual Input - CNN (DI-CNN). Furthermore, we greatly increase the labeled data volume of the LIDC-IDRI by incorporating rotation-based augmentation. We observe that the DI-CNN is the most accurate version of MP-CNN of several input dimensions in our comparison, thereby demonstrating that it is not necessary for both input paths to take imagery of different dimensions as was previously thought. Furthermore, we show that through data augmentation, it is possible to substantially increase the labeled data volume thereby allowing the DI-CNN algorithm to outperform a state-of-the-art hybrid CNN/Radiomic algorithm for classification of nodule malignancy.
590 $a School code: 0434.
650 4 $a Computer science. $3 523869
650 4 $a Neurosciences. $3 588700
650 4 $a Medical imaging. $3 3172799
650 4 $a Bioinformatics. $3 553671
653 $a Dual Input CNN
653 $a Convolutional neural network
653 $a Diagnostic classification
653 $a Lung nodule malignancy
653 $a CT Scan
690 $a 0984
690 $a 0574
690 $a 0317
690 $a 0715
710 2 $a University of Maryland, Baltimore County. $b Computer Science. $3 1018441
773 0 $t Masters Abstracts International $g 82-05.
790 $a 0434
791 $a M.S.
792 $a 2020
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28028937