2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 17 , Issue 2 , PP: 123-146, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Bahaa S. Rabi 1 * , Ayman S. Selmy 2 , Wael A. Mohamed 3
Doi: https://doi.org/10.54216/FPA.170210
Machine- and deep-learning techniques have been used in numerous real-world applications. One of the famous deep-learning methodologies is the Deep Convolutional Neural Network. AlexNet is a well-known global deep convolutional neural network architecture. AlexNet significantly contributes to solving different classification problems in different applications based on deep learning. Therefore, it is necessary to continuously improve the model to enhance its performance. This survey study formally defined the AlexNet architecture, presented information on current improvement solutions, and reviewed applications based on AlexNet improvements. This work also presents a simple survey based on a fusion of AlexNet with different machine-learning techniques for recent research in biomedical applications. In the survey results for about 11 research papers for both improvement and fusion techniques of AlexNet, it was clear that the fusion was the superior one with 99.72, and the improved one was 99.7%. In the conclusion and discussion section, there was a comparison between the improved techniques and fusion techniques of AlexNet and a proposal for future work on AlexNet development.
Artificial Intelligence , Deep Learning , AlexNet improvements , Machine learning , Convolutional Neural Networks , Fusion of AlexNet
[1] Ertel, W. Introduction to Artificial Intelligence; Springer: Berlin/Heidelberg, Germany, 2018.
[2] Rabi, Bahaa, et al. "Automatic Classification of Gastrointestinal Diseases Based on Machine Learning Techniques." 2019 29th International Conference on Computer Theory and Applications (ICCTA). IEEE, 2019
[3] Wang, Xizhao, Yanxia Zhao, and Farhad Pourpanah. "Recent advances in deep learning." International Journal of Machine Learning and Cybernetics, springer.11 (2020): 747-750.
[4] Han, X., Y. Zhong, L. Cao, and L. Zhang. 2017. “Pre-Trained AlexNet Architecture with Pyramid Pooling and Supervision for High Spatial Resolution Remote Sensing Image Scene Classification.” Remote Sensing 9: 848.
[5] Kumar, Ashnil, et al. "An ensemble of fine-tuned convolutional neural networks for medical image classification." IEEE journal of biomedical and health informatics 21.1 (2016): 31-40.
[6] Shen, Ranxin, Jiayi Wen, and Peiyi Zhu. "Blood Group Interpretation Algorithm Based on Improved AlexNet." Electronics 12.12 (2023): 2608.
[7] Kumar M, Arun, and Arvind Chakrapani. "Classification of ECG signal using FFT based improved AlexNet classifier." PLOS one 17.9 (2022): e0274225.
[8] He, Ming, et al. "An improved AlexNet model for automated skeletal maturity assessment using hand X-ray images." Future Generation Computer Systems 121 (2021): 106-113.
[9] Sadhana, S., and R. Mallika. "An intelligent technique for detection of diabetic retinopathy using improved AlexNet model based convolutional neural network." Journal of Intelligent & Fuzzy Systems 40.4 (2021): 7623-7634.
[10] Chen, Jun, Zhechao Wan, Jiacheng Zhang, Wenhua Li, Yanbing Chen, Yuebing Li, and Yue Duan. "Medical image segmentation and reconstruction of prostate tumor based on 3D AlexNet." Computer methods and programs in biomedicine 200 (2021): 105878.
[11] Lu, Siyuan, Shui-Hua Wang, and Yu-Dong Zhang. "Detection of abnormal brain in MRI via improved AlexNet and ELM optimized by chaotic bat algorithm." Neural Computing and Applications 33 (2021): 10799-10811.
[12] Shakarami, Ashkan, Hadis Tarrah, and Ali Mahdavi-Hormat. "A CAD system for diagnosing Alzheimer’s disease using 2D slices and an improved AlexNet-SVM method." Optik 212 (2020): 164237.
[13] Alaskar, Haya, et al. "The implementation of pretrained AlexNet on PCG classification." Intelligent Computing Methodologies: 15th International Conference, ICIC 2019, Nanchang, China, August 3–6, 2019, Proceedings, Part III 15. Springer International Publishing, 2019.
[14] Sathishkumar, R., et al. "An Improved Fusion Model from Google Net and AlexNet to Predict Breast Cancer using Deep Learning." 2023 International Conference on System, Computation, Automation and Networking (ICSCAN). IEEE, 2023.
[15] Naseer, Iftikhar, et al. "Lung Cancer Detection Using Modified AlexNet Architecture and Support Vector Machine." Computers, Materials & Continua 74.1 (2023).
[16] Sethy, Prabira Kumar, et al. "Lung cancer histopathological image classification using wavelets and AlexNet." Journal of X-Ray Science and Technology 31.1 (2023): 211-221.
[17] Kibriya, Hareem, et al. "A novel and effective brain tumor classification model using deep feature fusion and famous machine learning classifiers." Computational Intelligence and Neuroscience 2022.1 (2022): 7897669.
[18] Muhammad Hussain, Nadia, et al. "Accessing artificial intelligence for fetus health status using hybrid deep learning algorithm (AlexNet-SVM) on cardiotocographic data." Sensors 22.14 (2022): 5103.
[19] Abdelaziz, Ahmed., N., Alia. Skin Cancer Detection Using Deep Learning and Artificial Intelligence: Incorporated model of deep features fusion. Fusion: Practice and Applications, vol. 8, no. 2, 2022, pp. 08-15. DOI: https://doi.org/10.54216/FPA.080201
[20] Taneja, Harsh, Abhinav., Apoorv. Mangal, Himanshu. , Agarwal, Naman. Detection of Covid-19 using Cough Sounds. Fusion: Practice and Applications, vol. 7, no. 2, 2022, pp. 79-90. DOI: https://doi.org/10.54216/FPA.070202
[21] Atassi, Reem., Alhassan, Fuad., Djordjevic, Milan. A New Data Fusion Model for Medical Image Encryption in IoT Environment. Fusion: Practice and Applications, vol. 8, no. 1, 2022, pp. 16-26. DOI: https://doi.org/10.54216/FPA.080102
[22] Kaur, Surinder., Dinesh, Javalkar. Chaudhary, Gopal. , Khari, Manju. Breast Cancer Detection Using Deep Learning and Feature Decision Level Fusion. Fusion: Practice and Applications, vol. 8, no. 1, 2022, pp. 50-59. DOI: https://doi.org/10.54216/FPA.080105
[23] Z., Abedallah, Almajed, Rasha. , A., Saleh. Early Detection of Cardiovascular Diseases using Deep Learning Feature Fusion and MRI Image Analysis. Fusion: Practice and Applications, vol. 8, no. 2, 2022, pp. 16-24. DOI: https://doi.org/10.54216/FPA.080202
[24] Lee, Sheng Long, and Mohammad Reza Zare. "Biomedical compound figure detection using deep learning and fusion techniques." IET Image Processing 12.6 (2018): 1031-1037.
[25] Sinha, Divyanshu, et al. "Multimodal Deep Learning Analysis for Biomedical Data Fusion." Human Cancer Diagnosis and Detection Using Exascale Computing (2024): 53-69.
[26] Oza, Parita, et al. "Deep convolutional neural networks for computer-aided breast cancer diagnostic: a survey." Neural Computing and Applications, springer.34.3 (2022): 1815-1836.
[27] DeLiu, H.; Zhang, C.; Deng, Y.; Xie, B.; Liu, T.; Zhang, Z.; Li, Y.F. TransIFC: Invariant cues-aware feature concentration learning for efficient fine-grained bird image classification. IEEE Trans. Multimed. 2023, 1–14.
[28] Krichen, Moez. "Convolutional neural networks: A survey." Computers 12.8 (2023): 151.
[29] Sandia, Tomasz. "Review and comparison of commonly used activation functions for deep neural networks." Springer. Bio-inspired neurocomputing (2021): 203-224.
[30] Naseri, H.; Mehrdad, V. Novel CNN with investigation on accuracy by modifying stride, padding, kernel size and filter numbers. Multimed. Tools Appl. 2023, 82, 23673–23691.
[31] Tian, C.; Xu, Y.; Zuo, W.; Du, B.; Lin, C.W.; Zhang, D. Designing and training of a dual CNN for image denoising. Knowl. -Based Syst. 2021, 226, 106949.
[32] Jaramillo-Munera, Y.; Sepulveda-Cano, L.M.; Castro-Ospina, A.E.; Duque-Muñoz, L.; Martinez-Vargas, J.D. Classification of Epileptic Seizures Based on CNN and Guided Back-Propagation for Interpretation Analysis. In Proceedings of the International Conference on Smart Technologies, Systems and Applications, Cuenca, Ecuador, 16–18 November 2022; Springer: Berlin/Heidelberg, Germany, 2022; pp. 212–226.
[33] Ndong, P.S.B.; Adoni, W.Y.H.; Nahhal, T.; Kimpolo, C.; Krichen, M.; Byed, A.E.; Assayad, I.; Mutombo, F.K. A face-mask detection system based on deep learning convolutional neural networks. In Advances on Smart and Soft Computing: Proceedings of ICACIn 2021; Springer: Berlin/Heidelberg, Germany, 2021; pp. 273–283.
[34] Hui, T.W.; Tang, X.; Loy, C.C. A lightweight optical flow CNN—Revisiting data fidelity and regularization. IEEE Trans. Pattern Anal. Mach. Intell. 2020, 43, 2555–2569.
[35] Y. Keizhevsk, I Stuskever, E Hinton G, “ImageNet classification with deep convolutional neural networks[C].”, in Proceedings of the Advances in Neural Information Processing Systems. South Lake Tahoe, US, 2012, pp. 1097–1105.
[36] Guang-Bin H et al (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501.
[37] L. Yang, X. Ban, Y. Li, and G. Yang, “Multiple features fusion for facial expression recognition based on ELM,” Int. J. Embed. Syst., vol. 10, no. 3, pp. 181–187, 2018.
[38] El-Douh A, Lu S, Abdelhafeez A et al (2023) A neutrosophic multi-criteria model for evaluating sustainable soil enhancement methods and their cost 2 implications in construction. SMIJ 5(2):11.
[39] A. Abdelhafeez, H. K. Mohamed, A. Maher and N. A. Khalil, "A novel approach toward skin cancer classification through fused deep features and neutrosophic environment", Frontiers Public Health, vol. 11, pp. 1-15, Apr. 2023.
