[{“box”:0,”content”:”[if 992 equals=”Open Access”]n
n
Open Access
nn
n
n[/if 992]n
n
n
n
n
n
MD Nafees Chand, Dinesh Sahu
n
- n t
n
n
n[/foreach]
n
n[if 2099 not_equal=”Yes”]n
- [foreach 286] [if 1175 not_equal=””]n t
- Student, Professor Department of Computer Science and Engineering, RKDF Institute of Science and Technology, Bhopal, Department of Computer Science and Engineering, RKDF Institute of Science and Technology, Bhopal Madhya Pradesh, Madhya Pradesh India, India
n[/if 1175][/foreach]
n[/if 2099][if 2099 equals=”Yes”][/if 2099]n
Abstract
nClassifying images is useful in many domains, including the study of plant diseases and the analysis of human expressions. Image categorization employing the idea of a “deep neural network” helps to compact otherwise cumbersome photos. It is possible to classify images by using the idea of a “deep neural network”. Self-driving cars, medical diagnosis, automatic translation, etc., all make use of Deep Neural Networks. Recently, excellent results have been achieved via the use of deep neural networks to aid with picture classification. A neural network is a kind of pattern-recognition computer system. The structure of the human brain served as inspiration for its design, thus the name. Input, hidden, as well as output layers make up these layers. The Inception V3 model was used in the suggested work in order to categorize a picture into many categories such as animal, person, selfie, group shot, location, vehicle, etc. Rather than relying on picture feature extraction or image segmentation, this research provides a new way for more precise image categorization. An encouraging 99.6% accuracy was achieved in the intended task using proposed model Inception V3.
n
Keywords: Deep learning, Inception V3, image classification, neural network, image segmentation.
n[if 424 equals=”Regular Issue”][This article belongs to Journal of Telecommunication, Switching Systems and Networks(jotssn)]
n
n
n
n
n
nn[if 992 equals=”Open Access”] Full Text PDF Download[/if 992] n[if 992 not_equal=”Open Access”]
[/if 992]n[if 992 not_equal=”Open Access”] nnn[/if 992]nn[if 379 not_equal=””]n
Browse Figures
n
n[/if 379]n
References
n[if 1104 equals=””]n
- Gee, T., James, J., and Mark, W. (2017). Proceedings of International Conference on Image and Vision Computing.
- Wang, C., Chen, D., Hao, L., Liu, X., Zeng, Y., Chen, J., and Zhang, G. (2019). Pulmonary image classification based on inception-v3 transfer learning model. IEEE Access, 7, 146533-146541
- Shaha and M. Pawar, “Transfer Learning for Image Classification,” 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2018, pp. 656-660, doi: 10.1109/ICECA.2018.8474802.
- Tree bupachatsakul, Treesukon, and Suvit Poomrittigul. “Bacteria classification using image processing and deep learning.” 2019 34th international technical conference on circuits/systems, computers and communications (ITC-CSCC). IEEE, 2019.
- Chen, Guoming, et al. “Breast cancer image classification based on CNN and bit-plane slicing.” 2019 International Conference on Medical Imaging Physics and Engineering (ICMIPE). IEEE, 2019.
- P. K. K. J. H. Xiaolong Sun, “Novel Hybrid CNN-SVM Model for Recognition of Functional Magnetic Resonance Images,” in IEEE International Conference on Systems, Man, and Cybernetics (SMC), Korea, 2017
- Han, Ping, et al. “Polsar Image Classification based on Optimal Feature and Convolution Neural Network.” IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2020.
- Suganthi, M., and J. G. R. Sathiaseelan. “An exploratory of hybrid techniques on deep learning for image classification.” 2020 4th International Conference on Computer, Communication and Signal Processing (ICCCSP). IEEE, 2020.
- N. S. C. J. Q. S. L. T. W. a. B. L. Zhen Yu, “Hybrid Dermoscopy Image Classification Framework based on Deep Convolutional Neural Network and Fisher Vector,” IEEE, p. 301303, 2017
- Rajayogi, J. R., G. Manjunath, and G. Shobha. “Indian food image classification with transfer learning.” 2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS). Vol. 4. IEEE, 2019.
- Wang, Hao. “Garbage recognition and classification system based on convolutional neural network VGG16.” 2020 3rd International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE). IEEE, 2020.
- L. C. Y. K. L. Mingxing Duan, “A Hybrid deep learning CNN-ELM for age and gender classification,” ELSEVIER, pp. 448-461, 2018
- Wang, I-Hung, Kuang-Chyi Lee, and Shinn-Liang Chang. “Images Classification of Dogs and Cats using Fine-Tuned VGG Models.” 2020 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE). IEEE, 2020.
- Giraddi, Shantala, et al. “Flower classification using deep learning models.” 2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE). IEEE, 2020.
- M. O. T. l. Mert Copur, “Image Classification of Aerial Images using CNN-SVM,” in IEEE, Turkey, 2018
- Lakshmipriya, B., N. Pavithra, and D. Saraswathi. “Optimized Convolutional Neural Network based Colour Image Fusion.” 2020 International Conference on System, Computation, Automation and Networking (ICSCAN). IEEE, 2020.
- Bhandi, Vijayakumar, and KA Sumithra Devi. “Image retrieval by fusion of features from pre-trained deep convolution neural networks.” 2019 1st International Conference on Advanced Technologies in Intelligent Control, Environment, Computing and Communication Engineering (ICATIECE). IEEE, 2019.
- Armin, Edmund Ucok, Agus Bejo, and Risanuri Hidayat. “Vehicle type classification in surveillance image based on deep learning method.” 2020 3rd International Conference on Information and Communications Technology (ICOIACT). IEEE, 2020.
- Rezende, Vanessa, et al. “Image processing with convolutional neural networks for classification of plant diseases.” 2019 8th Brazilian Conference on Intelligent Systems (BRACIS). IEEE, 2019.
nn[/if 1104][if 1104 not_equal=””]n
- [foreach 1102]n t
- [if 1106 equals=””], [/if 1106][if 1106 not_equal=””],[/if 1106]
n[/foreach]
n[/if 1104]
nn
nn[if 1114 equals=”Yes”]n
n[/if 1114]
n
n
n
Journal of Telecommunication, Switching Systems and Networks
n
n
n
n
n
n
| Volume | 11 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 01 | |
| Received | May 5, 2024 | |
| Accepted | May 16, 2024 | |
| Published | May 27, 2024 |
n
n
n
n
n
n function myFunction2() {n var x = document.getElementById(“browsefigure”);n if (x.style.display === “block”) {n x.style.display = “none”;n }n else { x.style.display = “Block”; }n }n document.querySelector(“.prevBtn”).addEventListener(“click”, () => {n changeSlides(-1);n });n document.querySelector(“.nextBtn”).addEventListener(“click”, () => {n changeSlides(1);n });n var slideIndex = 1;n showSlides(slideIndex);n function changeSlides(n) {n showSlides((slideIndex += n));n }n function currentSlide(n) {n showSlides((slideIndex = n));n }n function showSlides(n) {n var i;n var slides = document.getElementsByClassName(“Slide”);n var dots = document.getElementsByClassName(“Navdot”);n if (n > slides.length) { slideIndex = 1; }n if (n (item.style.display = “none”));n Array.from(dots).forEach(n item => (item.className = item.className.replace(” selected”, “”))n );n slides[slideIndex – 1].style.display = “block”;n dots[slideIndex – 1].className += ” selected”;n }n”}]