Researchers have shown attention in Deep Learning approaches for Object Detection (OD) because of their implied strength in overcoming the drawbacks of traditional approaches that rely on handcrafted characteristics. This is due to the fact that Deep Learning approaches have the potential to overcome these drawbacks. Over the course of the last few years, Deep Learning algorithms have made significant strides forward in terms of object recognition. The most modern and efficient DL framework for object recognition is the topic of discussion in this research. Visual recognition systems, encompassing tasks such as picture categorization, picture native, and discovery, are indeed fundamental components of various applications across diverse domains. These systems have attracted significant academic interest due to their pivotal role in enabling a range of practical applications. Because of the significant progress that has been made in neural-networks, particularly in the arena of deep-learning, these visual recognition algorithms have acquired an exceptional level of performance. One of these fields, occupational diagnosis (OD), is one in which computer vision has been quite successful.
REFERENCES
1.
M. M.
Krishna
, M.
Neelima
, M.
Harshali
, and M. V. G.
Rao
. (2018
). “Image classification using Deep learning
.” International Journal of Engineering and Technology
, 7
(2
), 614
–617
. doi: .2.
L.
Zhang
, D.
Li
, and Q.
Guo
. (2020
). “Deep Learning from Spatio-Temporal Data Using Orthogonal Regularization Residual CNN for Air Prediction
.” IEEE Access
, 8
, 66037
–66047
. doi: .3.
P.
Wang
, E.
Fan
, and P.
Wang
. (2021
). “Comparative analysis of image classification algorithms based on traditional machine learning and deep learning
.” Pattern Recognition Letters
, 141
, 61
–67
. doi: .4.
S.
Kiranyaz
, O.
Avci
, O.
Abdeljaber
, T.
Ince
, M.
Gabbouj
, and D. J.
Inman
. (2021
). “1D convolutional neural networks and applications: A survey
.” Mechanical Systems and Signal Processing
, 151
, 1
–20
. doi: .5.
D.
Li
, L.
Deng
, and Z.
Cai
. (2020
). “Research on image classification method based on convolutional neural network
.” Neural Computing and Applications
, 7
. doi: .6.
B.
Huo
and F.
Yin
. (2015
). “Research on novel image classification algorithm based on multi-feature extraction and modified SVM classifier
.” International Journal of Smart Home
, 9
(9
), 103
–112
. doi: .7.
H.
Xu
, G.
Qi
, J.
Li
, M.
Wang
, K.
Xu
, and H.
Gao
. “Fine-grained Image Classification by Visual-Semantic Embedding
.” Pages 1043
–1049
.8.
L.
Sushma
and K. P.
Lakshmi
. (2020
). “An Analysis of Convolution Neural Network for Image Classification using Different Models
.” Volume 9
, Issue 10
, Pages 629
–637
.9.
P.
Kumar
and U.
Dugal
. (2020
). “TensorFlow Based Image Classification using Advanced Convolutional Neural Network
.” International Journal of Recent Technology and Engineering
, 8
(6
), 994
–998
. doi: .10.
M. A.
Abu
, N. H.
Indra
, A. H. A.
Rahman
, N. A.
Sapiee
, and I.
Ahmad
. (2019
). “A study on image classification based on deep learning and TensorFlow
.” International Journal of Engineering Research and Technology
, 12
(4
), 563
–569
.11.
K.
Chauhan
and S.
Ram
. (2018
). “Image Classification with Deep Learning and Comparison between Different Convolutional Neural Network Structures using TensorFlow and Keras
.” International Journal of Advanced Engineering Research and Development
, 5
(02
), 533
–538
.12.
B. K.
Jha
, G.
Sivasankari
G, and R.
Venugopal
K. (2021
). “E-Commerce Product Image Classification using Transfer Learning
.” Proceedings of the 5th International Conference on Computational Methodologies and Communication
, ICCMC
2021, pp. 904
–912
. doi: .13.
N.
Jmour
, S.
Zayen
, and A.
Abdelkrim
. (2018
). “Convolutional neural networks for image classification.” Proceedings of the 2018 International Conference on Advanced Systems and Electrical Technologies
(IC_ASET
2018), pp. 397
–402
. doi: .14.
Z.
Li
, X.
Zhu
, L.
Wang
, and P.
Guo
. (2018
). “Image Classification Using Convolutional Neural Networks and Kernel Extreme Learning Machines
.” Proceedings of the International Conference on Image Processing (ICIP)
, pp. 3009
–3013
. doi: .15.
M.
Xin
and Y.
Wang
. (2019
). “Research on image classification model based on deep convolutional neural network
.” Eurasip Journal on Image and Video Processing
, 2019
(1
). doi: .16.
A.
Patil
. (2021
). “Image Recognition using Machine Learning
.” SSRN Electronic Journal.
doi: .17.
S. S.
Sravya
, K. S. R.
Krishna
, and P. S.
Suhasini
. (2019
). “Image classification using supervised convolutional neural network
.” International Journal of Recent Technology and Engineering
, 8
(2
), 4505
–4507
. doi: .18.
Z.
Chen
, S.
Ai
, and C.
Jia
. (2019
). “Structure-aware deep learning for product image classification
.” ACM Transactions on Multimedia Computing, Communications, and Applications
, 15
(1s
). doi: .19.
C.
Zhang
, J.
Cheng
, and Q.
Tian
. (2018
). “Image-level classification by hierarchical structure learning with visual and semantic similarities
.” Information Sciences
, 422
, 271
–281
. doi: .20.
M. A.
Hossain
and M. S. Alam
Sajib
. (2019
). “Classification of Image using Convolutional Neural Network (CNN)
.” Global Journal of Computer Science and Technology
, 19
(2
), 13
–18
. doi: .21.
P. V.
Jithi
and A. T.
Nair
. (2013
). “Progressive visual cryptography with watermarking for meaningful shares
.” Proceedings of the 2013 IEEE International Multi-Conference on Automation, Computing, Control, Communication and Compressed Sensing
(iMac4s
2013), pp. 394
–401
. doi: .22.
S. A.
Oyewole
and O. O.
Olugbara
. (2018
). “Product image classification using Eigen Colour feature with ensemble machine learning
.” Egyptian Informatics Journal
, 19
(2
), 83
–100
. doi: .23.
Nemade
, V.
, Pathak
, S.
, Dubey
, A.K.
, Barhate
, D.
(2023
). Breast Mass Classification Using Convolutional Neural Network. In: Bhateja
, V.
, Sunitha
, K.V.N.
, Chen
, YW.
, Zhang
, YD.
(eds) Intelligent System Design. Lecture Notes in Networks and Systems
, vol 494
. Springer
, Singapore
. .24.
Bhandari
, S.
, Pathak
, S.
& Jain
, S.A.
A Literature Review of Early-Stage Diabetic Retinopathy Detection Using Deep Learning and Evolutionary Computing Techniques
. Arch Computat Methods Eng
(2022
). .25.
Gupta
, J.
, Pathak
, S.
& Kumar
, G.
Aquila coyote-tuned deep convolutional neural network for the classification of bare skinned images in websites
. Int. J. Mach. Learn. & Cyber.
13
, 3239
–3254
(2022
). .26.
Jadwal
, P.K.
, Jain
, S.
, Pathak
, S.
et al. Improved resampling algorithm through a modified oversampling approach based on spectral clustering and SMOTE
. Microsyst Technol
(2022
). .27.
Nemade
, V.
, Pathak
, S.
& Dubey
, A.K.
A Systematic Literature Review of Breast Cancer Diagnosis Using Machine Intelligence Techniques
. Arch Computat Methods Eng
29
, 4401
–4430
(2022
). 28.
Gupta
, J.
, Pathak
, S.
& Kumar
, G.
A hybrid optimization-tuned deep convolutional neural network for bare skinned image classification in websites
. Multimed Tools Appl
81
, 26283
–26305
(2022
). .
This content is only available via PDF.
© 2025 Author(s). Published under an exclusive license by AIP Publishing.
2025
Author(s)
You do not currently have access to this content.
