東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

An Artificial Intelligence Driven Fr...

Sanghvi, Harshal A.

Linked to FindBook

Google Book

Amazon

博客來

An Artificial Intelligence Driven Framework for Medical Imaging.

Record Type:	Electronic resources : Monograph/item
Title/Author:	An Artificial Intelligence Driven Framework for Medical Imaging./
Author:	Sanghvi, Harshal A.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	172 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-02, Section: B.
Contained By:	Dissertations Abstracts International85-02B.
Subject:	Computer engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30575869
ISBN:	9798380138499

An Artificial Intelligence Driven Framework for Medical Imaging.
Sanghvi, Harshal A.

An Artificial Intelligence Driven Framework for Medical Imaging. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 172 p.

Source: Dissertations Abstracts International, Volume: 85-02, Section: B.

Thesis (Ph.D.)--Florida Atlantic University, 2023.

The major objective of this dissertation was to create a framework which is used for medical image diagnosis. In this diagnosis, we brought classification and diagnosing of diseases through an Artificial Intelligence based framework, including COVID, Pneumonia, and Melanoma cancer through medical images. The algorithm ran on multiple datasets. A model was developed which detected the medical images through changing hyper-parameters.The aim of this work was to apply the new transfer learning framework DenseNet-201 for the diagnosis of the diseases and compare the results with the other deep learning models. The novelty in the proposed work was modifying the Dense Net 201 Algorithm, changing hyper parameters (source weights, Batch Size, Epochs, Architecture (number of neurons in hidden layer), learning rate and optimizer) to quantify the results. The novelty also included the training of the model by quantifying weights and in order to get more accuracy. During the data selection process, the data were cleaned, removing all the outliers. Data augmentation was used for the novel architecture to overcome overfitting and hence not producing false absurd results the computational performance was also observed. The proposed model results were also compared with the existing deep learning models and the algorithm was also tested on multiple datasets.In the proposed deep learning model, DenseNet-201, a convolutional neural network was used which was trained on the ImageNet dataset. Each layer was getting more accurate information from the previous layer. Since each layer receives feature maps from all preceding layers, the network was thinner and more compact. The growth rate k was the additional number of channels for each layer to have higher computational efficiency and memory efficiency. In the present work, composite learning factor strategy and data augmentation were included in the developed model. The graphical user interface (GUI) which was developed assisted in computational experience and adhered to the HIPAA Compliance. The GUI also provided different image observations which were generated through different visualization techniques. The proposed model gave us more accurate results which was not overfitting, had better computational performance and will assist clinicians with more visual data representation. The parameters were tuned, and the model complexity was calculated. The present work was validated by the medical fraternity.

ISBN: 9798380138499Subjects--Topical Terms:

621879
Computer engineering.
Subjects--Index Terms:

DenseNet-201

An Artificial Intelligence Driven Framework for Medical Imaging.
LDR:03662nmm a2200385 4500 001 2398527
005 20240812064659.5
006 m o d
007 cr#unu||||||||
008 251215s2023 ||||||||||||||||| ||eng d
020 $a 9798380138499
035 $a (MiAaPQ)AAI30575869
035 $a AAI30575869
040 $a MiAaPQ $c MiAaPQ
100 1 $a Sanghvi, Harshal A. $0 (orcid)0000-0001-6952-0504 $3 3768442
245 1 3 $a An Artificial Intelligence Driven Framework for Medical Imaging.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 172 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-02, Section: B.
500 $a Advisor: Agarwal, Ankur;Alhalabi, Bassem.
502 $a Thesis (Ph.D.)--Florida Atlantic University, 2023.
520 $a The major objective of this dissertation was to create a framework which is used for medical image diagnosis. In this diagnosis, we brought classification and diagnosing of diseases through an Artificial Intelligence based framework, including COVID, Pneumonia, and Melanoma cancer through medical images. The algorithm ran on multiple datasets. A model was developed which detected the medical images through changing hyper-parameters.The aim of this work was to apply the new transfer learning framework DenseNet-201 for the diagnosis of the diseases and compare the results with the other deep learning models. The novelty in the proposed work was modifying the Dense Net 201 Algorithm, changing hyper parameters (source weights, Batch Size, Epochs, Architecture (number of neurons in hidden layer), learning rate and optimizer) to quantify the results. The novelty also included the training of the model by quantifying weights and in order to get more accuracy. During the data selection process, the data were cleaned, removing all the outliers. Data augmentation was used for the novel architecture to overcome overfitting and hence not producing false absurd results the computational performance was also observed. The proposed model results were also compared with the existing deep learning models and the algorithm was also tested on multiple datasets.In the proposed deep learning model, DenseNet-201, a convolutional neural network was used which was trained on the ImageNet dataset. Each layer was getting more accurate information from the previous layer. Since each layer receives feature maps from all preceding layers, the network was thinner and more compact. The growth rate k was the additional number of channels for each layer to have higher computational efficiency and memory efficiency. In the present work, composite learning factor strategy and data augmentation were included in the developed model. The graphical user interface (GUI) which was developed assisted in computational experience and adhered to the HIPAA Compliance. The GUI also provided different image observations which were generated through different visualization techniques. The proposed model gave us more accurate results which was not overfitting, had better computational performance and will assist clinicians with more visual data representation. The parameters were tuned, and the model complexity was calculated. The present work was validated by the medical fraternity.
590 $a School code: 0119.
650 4 $a Computer engineering. $3 621879
650 4 $a Medical imaging. $3 3172799
653 $a DenseNet-201
653 $a Deep learning model
653 $a Graphical user interface
653 $a Visual data representation
653 $a Medical fraternity
690 $a 0464
690 $a 0574
690 $a 0800
710 2 $a Florida Atlantic University. $b Computer Engineering. $3 3427715
773 0 $t Dissertations Abstracts International $g 85-02B.
790 $a 0119
791 $a Ph.D.
792 $a 2023
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30575869