Improving Diabetic Retinopathy Classification: A MobileNet Feature-Based Transfer Learning with Logistic Regression Investigation

Taimingwang Liu; Chengzhangzheng Wu; Junqing Yang; Chenguang Liu; Anwar P.P.Abdul Majeed

doi:10.1007/978-3-031-70687-5_16

Improving Diabetic Retinopathy Classification: A MobileNet Feature-Based Transfer Learning with Logistic Regression Investigation

Taimingwang Liu, Chengzhangzheng Wu, Junqing Yang, Chenguang Liu, Anwar P.P.Abdul Majeed^*

^*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

Abstract

Diabetic retinopathy (DR) is a serious eye condition that can lead to blindness. Owing to the advancement of technology, computer-aided diagnosis enables clinicians to act swiftly in the diagnosis of DR. The study explores the efficacy of feature-based transfer learning in the classification of DR by examining the ability of two pre-trained convolutional neural networks architecture, i.e.,MobileNet and MobileNetV2 in extracting meaningful features from retina scanned images. The Logistic Regression (LR) is used to classify the different classes of DR from the extracted features. It was shown from the present study that the MobileNet+LR yielded a better classification of the classes. It further demonstrates its feasibility as a plausible tool for early detection and treatment of the disease.

Original language	English
Title of host publication	Robot Intelligence Technology and Applications 8 - Results from the 11th International Conference on Robot Intelligence Technology and Applications
Editors	Anwar P. P. Abdul Majeed, Eng Hwa Yap, Pengcheng Liu, Xiaowei Huang, Anh Nguyen, Wei Chen, Ue-Hwan Kim
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	147-153
Number of pages	7
ISBN (Print)	9783031706868
DOIs	https://doi.org/10.1007/978-3-031-70687-5_16
Publication status	Published - 2024
Event	11th International Conference on Robot Intelligence Technology and Applications, RiTA 2023 - Taicang, China Duration: 6 Dec 2023 → 8 Dec 2023

Publication series

Name	Lecture Notes in Networks and Systems
Volume	1133 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	11th International Conference on Robot Intelligence Technology and Applications, RiTA 2023
Country/Territory	China
City	Taicang
Period	6/12/23 → 8/12/23

Keywords

Computer-aided diagnosis
Deep learning
Diabetic retinopathy
Transfer learning

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-031-70687-5_16

Cite this

Liu, T., Wu, C., Yang, J., Liu, C., & Majeed, A. P. P. A. (2024). Improving Diabetic Retinopathy Classification: A MobileNet Feature-Based Transfer Learning with Logistic Regression Investigation. In A. P. P. Abdul Majeed, E. H. Yap, P. Liu, X. Huang, A. Nguyen, W. Chen, & U.-H. Kim (Eds.), Robot Intelligence Technology and Applications 8 - Results from the 11th International Conference on Robot Intelligence Technology and Applications (pp. 147-153). (Lecture Notes in Networks and Systems; Vol. 1133 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-70687-5_16

Liu, Taimingwang ; Wu, Chengzhangzheng ; Yang, Junqing et al. / Improving Diabetic Retinopathy Classification : A MobileNet Feature-Based Transfer Learning with Logistic Regression Investigation. Robot Intelligence Technology and Applications 8 - Results from the 11th International Conference on Robot Intelligence Technology and Applications. editor / Anwar P. P. Abdul Majeed ; Eng Hwa Yap ; Pengcheng Liu ; Xiaowei Huang ; Anh Nguyen ; Wei Chen ; Ue-Hwan Kim. Springer Science and Business Media Deutschland GmbH, 2024. pp. 147-153 (Lecture Notes in Networks and Systems).

@inproceedings{c8f658cc229e4dec853fc84857878977,

title = "Improving Diabetic Retinopathy Classification: A MobileNet Feature-Based Transfer Learning with Logistic Regression Investigation",

abstract = "Diabetic retinopathy (DR) is a serious eye condition that can lead to blindness. Owing to the advancement of technology, computer-aided diagnosis enables clinicians to act swiftly in the diagnosis of DR. The study explores the efficacy of feature-based transfer learning in the classification of DR by examining the ability of two pre-trained convolutional neural networks architecture, i.e.,MobileNet and MobileNetV2 in extracting meaningful features from retina scanned images. The Logistic Regression (LR) is used to classify the different classes of DR from the extracted features. It was shown from the present study that the MobileNet+LR yielded a better classification of the classes. It further demonstrates its feasibility as a plausible tool for early detection and treatment of the disease.",

keywords = "Computer-aided diagnosis, Deep learning, Diabetic retinopathy, Transfer learning",

author = "Taimingwang Liu and Chengzhangzheng Wu and Junqing Yang and Chenguang Liu and Majeed, \{Anwar P.P.Abdul\}",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; 11th International Conference on Robot Intelligence Technology and Applications, RiTA 2023 ; Conference date: 06-12-2023 Through 08-12-2023",

year = "2024",

doi = "10.1007/978-3-031-70687-5\_16",

language = "English",

isbn = "9783031706868",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "147--153",

editor = "\{Abdul Majeed\}, \{Anwar P. P.\} and Yap, \{Eng Hwa\} and Pengcheng Liu and Xiaowei Huang and Anh Nguyen and Wei Chen and Ue-Hwan Kim",

booktitle = "Robot Intelligence Technology and Applications 8 - Results from the 11th International Conference on Robot Intelligence Technology and Applications",

}

Liu, T, Wu, C, Yang, J, Liu, C & Majeed, APPA 2024, Improving Diabetic Retinopathy Classification: A MobileNet Feature-Based Transfer Learning with Logistic Regression Investigation. in APP Abdul Majeed, EH Yap, P Liu, X Huang, A Nguyen, W Chen & U-H Kim (eds), Robot Intelligence Technology and Applications 8 - Results from the 11th International Conference on Robot Intelligence Technology and Applications. Lecture Notes in Networks and Systems, vol. 1133 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 147-153, 11th International Conference on Robot Intelligence Technology and Applications, RiTA 2023, Taicang, China, 6/12/23. https://doi.org/10.1007/978-3-031-70687-5_16

Improving Diabetic Retinopathy Classification: A MobileNet Feature-Based Transfer Learning with Logistic Regression Investigation. / Liu, Taimingwang; Wu, Chengzhangzheng; Yang, Junqing et al.
Robot Intelligence Technology and Applications 8 - Results from the 11th International Conference on Robot Intelligence Technology and Applications. ed. / Anwar P. P. Abdul Majeed; Eng Hwa Yap; Pengcheng Liu; Xiaowei Huang; Anh Nguyen; Wei Chen; Ue-Hwan Kim. Springer Science and Business Media Deutschland GmbH, 2024. p. 147-153 (Lecture Notes in Networks and Systems; Vol. 1133 LNNS).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - Improving Diabetic Retinopathy Classification

T2 - 11th International Conference on Robot Intelligence Technology and Applications, RiTA 2023

AU - Liu, Taimingwang

AU - Wu, Chengzhangzheng

AU - Yang, Junqing

AU - Liu, Chenguang

AU - Majeed, Anwar P.P.Abdul

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

PY - 2024

Y1 - 2024

N2 - Diabetic retinopathy (DR) is a serious eye condition that can lead to blindness. Owing to the advancement of technology, computer-aided diagnosis enables clinicians to act swiftly in the diagnosis of DR. The study explores the efficacy of feature-based transfer learning in the classification of DR by examining the ability of two pre-trained convolutional neural networks architecture, i.e.,MobileNet and MobileNetV2 in extracting meaningful features from retina scanned images. The Logistic Regression (LR) is used to classify the different classes of DR from the extracted features. It was shown from the present study that the MobileNet+LR yielded a better classification of the classes. It further demonstrates its feasibility as a plausible tool for early detection and treatment of the disease.

AB - Diabetic retinopathy (DR) is a serious eye condition that can lead to blindness. Owing to the advancement of technology, computer-aided diagnosis enables clinicians to act swiftly in the diagnosis of DR. The study explores the efficacy of feature-based transfer learning in the classification of DR by examining the ability of two pre-trained convolutional neural networks architecture, i.e.,MobileNet and MobileNetV2 in extracting meaningful features from retina scanned images. The Logistic Regression (LR) is used to classify the different classes of DR from the extracted features. It was shown from the present study that the MobileNet+LR yielded a better classification of the classes. It further demonstrates its feasibility as a plausible tool for early detection and treatment of the disease.

KW - Computer-aided diagnosis

KW - Deep learning

KW - Diabetic retinopathy

KW - Transfer learning

UR - https://www.scopus.com/pages/publications/85211316910

U2 - 10.1007/978-3-031-70687-5_16

DO - 10.1007/978-3-031-70687-5_16

M3 - Conference Proceeding

AN - SCOPUS:85211316910

SN - 9783031706868

T3 - Lecture Notes in Networks and Systems

SP - 147

EP - 153

BT - Robot Intelligence Technology and Applications 8 - Results from the 11th International Conference on Robot Intelligence Technology and Applications

A2 - Abdul Majeed, Anwar P. P.

A2 - Yap, Eng Hwa

A2 - Liu, Pengcheng

A2 - Huang, Xiaowei

A2 - Nguyen, Anh

A2 - Chen, Wei

A2 - Kim, Ue-Hwan

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 6 December 2023 through 8 December 2023

ER -

Liu T, Wu C, Yang J, Liu C, Majeed APPA. Improving Diabetic Retinopathy Classification: A MobileNet Feature-Based Transfer Learning with Logistic Regression Investigation. In Abdul Majeed APP, Yap EH, Liu P, Huang X, Nguyen A, Chen W, Kim UH, editors, Robot Intelligence Technology and Applications 8 - Results from the 11th International Conference on Robot Intelligence Technology and Applications. Springer Science and Business Media Deutschland GmbH. 2024. p. 147-153. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-70687-5_16

Improving Diabetic Retinopathy Classification: A MobileNet Feature-Based Transfer Learning with Logistic Regression Investigation

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Keywords

UN SDGs

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