Transfer Learning in CNNs for Image Recognition in RGB Images

References

[1] Puneet Kaushik, Mohit Jain , Gayatri Patidar, Paradayil Rhea Eapen, Chandra Prabha Sharma (2018). Smart Floor Cleaning Robot Using Android. International Journal of Electronics Engineering. https://www.csjournals.com/IJEE/PDF10- 2/64.%20Puneet.pdf

[2] West, J., & Bhattacharya, M. (2016). Intelligent financial fraud detection: A comprehensive review. Computers & Security, 57, 47–66. https://doi.org/10.1016/j.cose.2015.09.005

[3] Kaushik, P.; Jain, M.: Design of low power CMOS low pass filter for biomedical application. J. Electr. Eng. Technol. (IJEET) 9(5) (2018)

[4] Bauer, S., Wiest, R., Nolte, L. P., & Reyes, M. (2013). A survey of MRI-based medical image analysis for brain tumour studies. Physics in Medicine & Biology, 58(13), R97–R129. https://doi.org/10.1088/0031-9155/58/13/R97

[5] Dal Pozzolo, A., Boracchi, G., Caelen, O., Alippi, C., & Bontempi, G. (2017). Credit card fraud detection: A realistic modeling and a novel learning strategy. IEEE Transactions on Neural Networks and Learning Systems, 29(8), 3784–3797. https://doi.org/10.1109/TNNLS.2017.2736643

[6] Puneet Kaushik, Mohit Jain, Aman Jain, “A Pixel-Based Digital Medical Images Protection Using Genetic Algorithm,” International Journal of Electronics and Communication Engineering, ISSN 0974-2166 Volume 11, Number 1, pp. 31-37, (2018).

[7] Charron, O., Lallement, A., Jarnet, D., Noblet, V., Clavier, J. B., & Meyer, P. (2018). Automatic detection and segmentation of brain metastases on multimodal MR images with a deep convolutional neural network. Computers in Biology and Medicine, 95, 43–54. https://doi.org/10.1016/j.compbiomed.2018.02.004

[8] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.

[9] Raymaekers, J., Verbeke, W., & Verdonck, T. (2021). Weight-of-evidence 2.0 with shrinkage and spline-binning. arXiv preprint arXiv:2101.01494. Retrieved from https://arxiv.org/abs/2101.01494

[10] Kaushik, P., Jain, M., & Shah, A. (2018). A Low Power Low Voltage CMOS Based Operational Transconductance Amplifier for Biomedical Application.

[11] Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.-M., & Larochelle, H. (2017). Brain tumour segmentation with deep neural networks. Medical Image Analysis, 35, 18–31. https://doi.org/10.1016/j.media.2016.05.004

[12] InsiderFinance Wire. (2021). Logistic regression: A simple powerhouse in fraud detection. Medium. Retrieved from https://wire.insiderfinance.io/logistic-regression-a-simple-powerhouse-in-fraud-detection-15ab984b2102

[13] Puneet Kaushik, Mohit Jain. ―A Low Power SRAM Cell for High Speed ApplicationsUsing 90nm Technology.‖ Csjournals.Com 10, no. 2 (December 2018): 6.https://www.csjournals.com/IJEE/PDF10-2/66.%20Puneet.pdf

[14] Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. W. L. (2018). Artificial intelligence in radiology. Nature Reviews Cancer, 18(8), 500–510. https://doi.org/10.1038/s41568-018-0016-5

[15] Jain, M., & None Arjun Srihari. (2023). House price prediction with Convolutional Neural Network (CNN). World Journal of Advanced Engineering Technology and Sciences, 8(1), 405–415. https://doi.org/10.30574/wjaets.2023.8.1.0048

[16] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539

[17] Jain, M., & Shah, A. (2022). Machine Learning with Convolutional Neural Networks (CNNs) in Seismology for Earthquake Prediction. Iconic Research and Engineering Journals, 5(8), 389–398. https://www.irejournals.com/paper-details/1707057

[18] Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60–88. https://doi.org/10.1016/j.media.2017.07.005

[19] Bhat, N. (2019). Fraud detection: Feature selection-over sampling. Kaggle. Retrieved from https://www.kaggle.com/code/nareshbhat/fraud-detection-feature-selection-over-sampling

[20] Ristani, E., Solera, F., Zou, R., Cucchiara, R., & Tomasi, C. (2016). Performance measures and a data set for multi-target, multi-camera tracking. In Proceedings of the European Conference on Computer Vision Workshops (ECCVW).

[21] Mohit Jain and Arjun Srihari (2023). House price prediction with Convolutional Neural Network (CNN). https://wjaets.com/sites/default/files/WJAETS-2023-0048.pdf

[22] Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems (NIPS).

[23] Kayalibay, Baris, et al. “CNN-Based Segmentation of Medical Imaging Data.” ArXiv:1701.03056 [Cs], 25 July 2017, arxiv.org/abs/1701.03056.

[24] Shorten, Connor, and Taghi M. Khoshgoftaar. “A Survey on Image Data Augmentation for Deep Learning.” Journal of Big Data, vol. 6, no. 1, 6 July 2019, journalofbigdata.springeropen.com/articles/10.1186/s40537-019-0197-0, https://doi.org/10.1186/s40537-019-0197-0.

[25] L. Wang, W. Chen, W. Yang, F. Bi and F. R. Yu, "A State-of-the-Art Review on Image Synthesis With Generative Adversarial Networks," in IEEE Access, vol. 8, pp. 63514-63537, 2020, doi: 10.1109/ACCESS.2020.2982224.

[26] Kaushik, P., & Jain, M. A Low Power SRAM Cell for High Speed Applications Using 90nm Technology. Csjournals. Com, 10. https://www.csjournals.com/IJEE/PDF10-2/66.%20Puneet.pdf

[27] K. Maharana, S. Mondal, and B. Nemade, “A review: Data pre-processing and data augmentation techniques,” Global Transitions Proceedings, vol. 3, no. 1, pp. 91–99, Jun. 2022, doi: 10.1016/j.gltp.2022.04.020.

[28] L. Jen and Y.-H. Lin, “A Brief Overview of the Accuracy of Classification Algorithms for Data Prediction in Machine Learning Applications,” Journal of Applied Data Sciences, vol. 2, no. 3, pp. 84–92, 2021, doi: 10.47738/jads.v2i3.38.

[29] Kaushik P, Jain M, Jain A (2018) A pixel-based digital medical images protection using genetic algorithm. Int J Electron Commun Eng 11:31–37

[30] Louis, D. N., Perry, A., Reifenberger, G., von Deimling, A., Figarella-Branger, D., Cavenee, W. K., Ohgaki, H., Wiestler, O. D., Kleihues, P., & Ellison, D. W. (2016). The 2016 World Health Organization classification of tumours of the central nervous system: A summary. Acta Neuropathologica, 131(6), 803–820. https://doi.org/10.1007/s00401-016-1545-1

[31] S. A. Hicks et al., “On evaluation metrics for medical applications of artificial intelligence,” Sci Rep, vol. 12, no. 1, pp. 1–9, Dec. 2022, doi: 10.1038/s41598-022-09954-8.

[32] Pallud, J., Fontaine, D., Duffau, H., Mandonnet, E., Sanai, N., Taillandier, L., Peruzzi, P., Guillevin, R., Bauchet, L., Bernier, V., Baron, M.-H., Guyotat, J., & Capelle, L. (2010). Natural history of incidental World Health Organization grade II gliomas. Annals of Neurology, 68(5), 727–733. https://doi.org/10.1002/ana.22106

[33] Pereira, S., Pinto, A., Alves, V., & Silva, C. A. (2016). Brain tumour segmentation using convolutional neural networks in MRI images. IEEE Transactions on Medical Imaging, 35(5), 1240–1251. https://doi.org/10.1109/TMI.2016.2538465

[34] Patel, H., & Zaveri, M. (2011). Credit card fraud detection using neural network. International Journal of Innovative Research in Computer and Communication Engineering, 1(2), 1–6. https://www.ijircce.com/upload/2011/october/1_Credit.pdf

[35] Kaushik, P., & Jain, M. (2018). Design of low power CMOS low pass filter for biomedical application. International Journal of Electrical Engineering & Technology (IJEET), 9(5).

[36] Alom, Md Zahangir, et al. “The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches.” ArXiv:1803.01164 [Cs], 12 Sept. 2018, arxiv.org/abs/1803.01164.

[37] Wang, Weibin, et al. “Medical Image Classification Using Deep Learning.” Intelligent Systems Reference Library, 19 Nov. 2019, pp. 33–51, https://doi.org/10.1007/978-3-030-32606-7_3.

[38] Nabati, R., & Qi, H. (2019). "RRPN: Radar Region Proposal Network for Object Detection in Autonomous Vehicles." 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan, 2019, pp. 3093-3097, doi: 10.1109/ICIP.2019.8803392.

[39] Kaushik, P., Jain, M., Patidar, G., Eapen, P. R., & Sharma, C. P. (2018). Smart Floor Cleaning Robot Using Android. International Journal of Electronics Engineering. https://www. csjournals. com/IJEE/PDF10-2/64.% 20Puneet. pdf.

[40] Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 (pp. 234–241). Springer. https://doi.org/10.1007/978-3-319-24574-4_28

[41] Stupp, R., Taillibert, S., Kanner, A., Read, W., Steinberg, D. M., Lhermitte, B., Toms, S., Idbaih, A., Ahluwalia, M. S., Fink, K., Di Meco, F., Lieberman, F., Zhu, J.-J., Stragliotto, G., Tran, D. D., Brem, S., Hottinger, A., Kirson, E. D., Lavy-Shahaf, G., … Hegi, M. E. (2017). Effect of tumor-treating fields plus maintenance temozolomide vs maintenance temozolomide alone on survival in patients with glioblastoma: A randomized clinical trial. JAMA, 318(23), 2306–2316. https://doi.org/10.1001/jama.2017.18718

[42] Raymaekers, J., Verbeke, W., & Verdonck, T. (2021). Weight-of-evidence 2.0 with shrinkage and spline-binning. arXiv preprint arXiv:2101.01494. Retrieved from https://arxiv.org/abs/2101.01494

@article{1708846,
      author = {Raj Agarwal, Kunal Doshi, Ayushi Upreti, Aniket Tripathi, Shruti Sinha},
      title = {Transfer Learning in CNNs for Image Recognition in RGB Images},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {7},
      number = {6},
      pages = {550-564},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1708846.pdf},
      abstract = {The advancement in computer vision technologies has made Convolutional Neural Networks (CNNs) a fundamental building block in image recognition applications. Successful yet, training CNNs from scratch is mostly infeasible due to the fact that training CNNs necessitates huge amounts of labeled data as well as enormous computational power. Transfer learning addresses both gaps by allowing the pre-trained architectures to be leveraged to fine-tune them in new image recognition tasks with little data and computational power. This paper introduces transfer learning in CNN-based image recognition with special emphasis on RGB images using well-known architectures VGGNet, ResNet, and Inception. Through them, it is described how transfer learning enables efficient and effective image classification using little data and computational power. Two major methods?feature extraction as well as fine-tuning?and their application and realization as well as usage in different situations are described thoroughly. The paper further identifies key challenges such as domain mismatch, overfitting, and computational limitations as well as suggests potential optimizations to alleviate them. Experiments confirm that transfer learning drastically improves model performance while reducing training time as well as data requirements. Findings attest to the effectiveness as well as flexibility and versatility provided by transfer learning rendering it a useful technique to apply on different occasions and academic and business settings alike. Finally but not least, this work highlights how transfer learning democratizes deep learning by enabling high-performance image recognition even in data-poor situations enabling greater deployment and application of AI-powered solutions in various research and industry environments.},
      keywords = {Transfer Learning, Convolutional Neural Networks (CNN), Image Classification, Analysis of RGB Images, Feature Extraction Methods, Fine-Tuning Methods, Deep Learning Optimization Methods, Applications in Computer Vision, Pre-trained CNN Models, Reuse and Adjustment of Models, Efficient Learning using Fewer Data, Generalization of Neural Network, Detection of Visual Patterns, Domain Adaptation, Training Efficiency, ResNet, VGGNet, Inception Architectures, Learning using Small Datasets, Accessibility to AI},
      month = {December},
  }