Bibliographic Analysis on Image Classification using Transfer Learning


  • Dhatrika Bhagyalaxmi Koneru Lakshmaiah Education Foundation,Guntur,Andhra Pradesh,India
  • B. Sekhar Babu Research Scholar,, Department of CSE,Scholar, Koneru Lakshmaiah Education Foundation,Guntur,Andhra Pradesh,India


Deep Learning, Convolutional Neural Networks, Transfer Learning, Image Classification, Bibliographic analysis


In this paper we conducted a systematic literature study on image classification using transfer learning techniques during last five years using bibliometric methods. Transfer learning is an important method to classify images using existing neural network architectures. These architectures were developed using Convolutional Neural Networks concept of Deep Learning. The analysis is carried on the standard SCOPUS dataset and analyzed using VOSviewer software. The study is limited to publications in English language on “Transfer Learning”, “Deep Learning”, “Convolutional Neural Network” and “Image Classification” covering Computer and Engineering subjects during the years 2017 to 2021. This paper’s research will aid relevant researchers in understanding the current state of development and trends in this area. Only a few literature studies have tracked the growth of this field, and even fewer have used bibliometric approaches or scientific maps. As a result, this work provides an updated evaluation of this fast-growing subject, using a bibliometric technique to highlight new breakthroughs using scientific maps, and a unique visualization to depict the thematic network structure and progress.


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Diagnostic criteria and classification of hyperglycemia first detected in pregnancy: a World Health Organization guideline. criteria-and-classification-of-hyperglycaemia-first-detected-in- pregnancy-A-World-Health-Organization-Guideline.pdf?sequence=1. Diabetes Res Clin Pract. 2014:341–363.

Proceedings of the fourth international work-shop-conference on gestational diabetes mellitus. Metzger BE, Coustan DR. Diabetes Care. 1998; Suppl 2:0–167.

Scientometric data files. A comprehensive set of indicators on 2649 journals and 96 countries in all major science fields and subfields 1981-1985. Schubert A, Glänzel W, Braun T. Scientometrics. 1989; 16:3–478.

Coverage analysis of Scopus: a journal metric approach. de Moya- Anegón F, Chinchilla-Rodríguez Z, Vargas-Quesada B, Corera- Álvarez E, Muñoz-Fernández F, González-Molina A. Scientometrics. 2007; 73:53–78.

The role of energy nutrients, foods, and dietary patterns in the development of gestational diabetes mellitus: a systematic review of observational studies. Schoenaker D, Mishra G, Callaway L, Soedamah-Muthu S. Diabetes Care. 2015; 39:16–23.

top-cited scientific papers in limb prosthetics. Eshraghi A, Osman N, Gholizadeh H, Ali S, Shadgan B. Bio Med Eng OnLine. 2013; 12:119.

Trends in the quality of highly cited surgical research over the past 20 years. Brooke B, Nathan H, Pawlik T. Ann Surg. 2009; 249:162–167.

Deepak, S., Ameer, P.M. Automated Categorization of Brain Tumor from MRI Using CNN features and SVM. J Ambient Intell Human Comput 12, 8357–8369 (2021). 02568-w

Bin Wang, Bing Xue, and Mengjie Zhang. 2021. A transfer learning based evolutionary deep learning framework to evolve convolutional neural networks. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO ‘21). Association for Computing Machinery, New York, NY, USA, 287–288. DOI:

Choudhary, Tejalal & Mishra, Vipul & Goswami, Anurag & Sarangapani, Jag. (2021). A Transfer Learning with Structured Filter Pruning Approach for Improved Breast Cancer Classification on Point-of-Care Devices. Computers in Biology and Medicine. 134. 10.1016/j.compbiomed.2021.104432.

V. Mishra, A. K. V and M. Arora, “Deep convolution neural network based automatic multi-class classification of skin cancer from dermoscopic images,” 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS), 2021, pp. 800- 805, doi: 10.1109/ICICCS51141.2021.9432160.

E. Hirani, V. Magotra, J. Jain and P. Bide, “Plant Disease Detection Using Deep Learning,” 2021 6th International Conference for Convergence in Technology (I2CT), 2021, pp. 1-4, doi: 10.1109/I2CT51068.2021.9417910.

Srdjan Sladojevic, Marko Arsenovic, Andras Anderla, Dubravko Culibrk and Darko Stefanovic, “Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification”, Computational Intelligence and Neuroscience, 2016.

Sue Han Lee, Hervé Goëau, Pierre Bonnet and Alexis Joly, “New perspectives on plant disease characterization based on deep learning”, Computers and Electronics in Agriculture, vol. 170, 2020, ISSN 0168-1699.

Jayme G.A. Barbedo, Factors influencing the use of deep learning for plant disease recognition Biosystems Engineering, vol. 172, no. 2018, pp. 84-91, ISBN 1537–5110.

Maeda-Gutiérrez, Valeria & Galván Tejada, Carlos & Zanella Calzada, Laura & Celaya Padilla, Jose & Galván Tejada, Jorge & Gamboa-Rosales, Hamurabi & Luna-Garcia, Huizilopoztli & Magallanes-Quintanar, Rafael & Carlos, Guerrero-Mendez & Olvera- Olvera, Carlos. (2020). Comparison of Convolutional Neural Network Architectures for Classification of Tomato Plant Diseases. Applied Sciences. 10. 1245. 10.3390/app10041245.

Logeswari, T., Karnan, M. An improved implementation of brain tumor detection using segmentation based on hierarchical self organizing map (2010) International Journal of Computer Theory and Engineering, 2 (4), p. 591.

Tandel, Gopal S., Mainak Biswas, Omprakash G. Kakde, Ashish Tiwari, Harman S. Suri, Monica Turk, John R. Laird, Christopher K. Asare, Annabel A. Ankrah, N. N. Khanna, B. K. Madhusudhan, Luca Saba, and Jasjit S. Suri 2019. "A Review on a Deep Learning Perspective in Brain Cancer Classification" Cancers 11, no. 1: 111.

Zeineldin, R.A., Karar, M.E., Coburger, J. et al. DeepSeg: deep neural network framework for automatic brain tumor segmentation using magnetic resonance FLAIR images. Int J CARS 15, 909–920 (2020).

Swati, Zar & Zhao, Qinghua & Kabir, Muhammad & Ali, Farman & Ali, Zakir & Ahmad, Saeed & Lu, Jianfeng. (2019). Brain tumor classification for MR images using transfer learning and fine-tuning. Computerized Medical Imaging and Graphics. 75. 34-46. 10.1016/j.compmedimag.2019.05.001.

M. Gurbină, M. Lascu and D. Lascu, "Tumor Detection and Classification of MRI Brain Image using Different Wavelet Transforms and Support Vector Machines," 2019 42nd International Conference on Telecommunications and Signal Processing (TSP), 2019, pp. 505-508, doi: 10.1109/TSP.2019.8769040.

Zitt M, Ramanana-Rahary S, Bassecoulard E. Relativity of citation performance and excellence measures: from cross-field to cross-scale effects of field-normalization. Scientometrics. 2005;63(2):373–401.

Li LL, et al. Global stem cell research trend: bibliometric analysis as a tool for mapping of trends from 1991 to 2006. Scientometrics. 2009;80(1):39–58.

Fooladi M, et al. Do criticisms overcome the praises of journal impact factor? Asian Soc Sci. 2013; 9(5):176–82.

Ale Ebrahim N, et al. Equality of google scholar with web of science citations: case of Malaysian engineering highly cited papers. Mod Appl Sci. 2014; 8(5):63–9.

Gomez-Jauregui V, et al. Information management and improvement of citation indices. Int J Inf Manage.2014; 34(2):257–71.

Andrea Caroppo, Alessandro Leone, Pietro Siciliano, Deep transfer learning approaches for bleeding detection in endoscopy images, Computerized Medical Imaging and Graphics, Volume 88, 2021, 101852, ISSN 0895-6111,

Ghosh, T., Chakareski, J. Deep Transfer Learning for Automated Intestinal Bleeding Detection in Capsule Endoscopy Imaging. J Digit Imaging 34, 404–417 (2021). 00428-3.

H.C. Shin, H.R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, R.M. Summers, Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE Transactions on Medical Imaging 35(5), 1285 (2016).

D. Zhang, Z. Liu and X. Shi, “Transfer Learning on EfficientNet for Remote Sensing image Classification,” 2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE), 2020, pp. 2255-2258, doi: 10.1109/ICMCCE51767.2020.00489.

W. Meng and M. Tia, “Unmanned Aerial Vehicle Classification and Detection Based on Deep Transfer Learning,” 2020 International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI), 2020, pp. 280-285, doi: 10.1109/ICHCI51889.2020.00067.

C Aker and S. Kalkan, “Using deep networks for drone detection”, In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1-6, 2017

C Tan, F Sun, T Kong, W Zhang, C Yang and C. Liu, A survey on deep transfer learning. International Conference on Artificial Neural Networks. ICANN, pp. 270-279, 2018.

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How to Cite

D. . Bhagyalaxmi and B. . Sekhar Babu, “Bibliographic Analysis on Image Classification using Transfer Learning”, Int J Intell Syst Appl Eng, vol. 11, no. 2, pp. 104–111, Feb. 2023.



Research Article