Feature Selection and Classification with the Annealing Optimization Deep Learning for the Multi-Modal Image Processing
DOI:
https://doi.org/10.69996/jcai.2024015Keywords:
Feature Selection, Classification, Annealing, Multi-Modal Image, OptimizationAbstract
This paper investigates and compares various feature selection algorithms within the context of image processing across multiple datasets. The study evaluates Seahorse Annealing Optimization for Feature Selection (SAO-FS), Genetic Algorithms (GA), CNN + Feature Fusion Network, and Lasso Regression on distinct image datasets—medical images, satellite images, MRI scans, and microscopy images. Performance metrics including accuracy, precision, recall, and computational time are analyzed to assess the efficacy of each algorithm in optimizing feature subsets for classification tasks. SAO-FS demonstrates superior performance in medical image classification with an accuracy of 92.5%, showcasing its ability to achieve high precision and recall rates critical for medical diagnostics. GA proves effective for satellite imagery with an accuracy of 87.3%, while the CNN + Feature Fusion Network excels in MRI scans with 89.8% accuracy. Lasso Regression, though slightly less accurate at 85.6%, efficiently selects features for microscopy images within a shorter computational time. These findings highlight the strengths and trade-offs of each algorithm across different image processing domains, providing insights for selecting appropriate feature selection methods tailored to specific imaging applications.
References
[1] M. H.Abid, R. Ashraf, T.Mahmood and C.N. Faisal, “Multi-modal medical image classification using deep residual network and genetic algorithm,” Plos one, vol.18, no.6, pp.e0287786, 2023.
[2] W. N.Ismail, F. R. PP and M. A. Ali, “A meta-heuristic multi-objective optimization method for alzheimer’s disease detection based on multi-modal data,” Mathematics, vol.11, no.4, pp.957,2023.
[3] W.Liu, X.Lu and Y. Wei, “AMFF-net: adaptive multi-modal feature fusion network for image classification,” Multimedia Tools and Applications, vol.83, no.6, pp.17069-17091, 2024.
[4] H. Wang, Y.Chen, C. Ma, J. Avery, L. Hull et al., “Multi-modal learning with missing modality via shared-specific feature modelling,” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15878-15887, 2023.
[5] V.Rai, G.Gupta, S.Joshi, R.Kumar and A. Dwivedi, “LSTM-based adaptive whale optimization model for classification of fused multimodality medical image,” Signal, Image and Video Processing, vol.17, no.5, pp.2241-2250, 2023.
[6] T.Liu, J.Huang, T.Liao, R.Pu, S.Liu et al., “A hybrid deep learning model for predictingmolecular subtypes of human breast cancer using multimodal data,” Irbm, vol.43, no.1, pp.62-74,2022.
[7] M.R.Islam, A.A. Lima, S.C.Das, M.F. Mridha, A.R. Prodeep and Y. Watanobe, “A comprehensive survey on the process, methods, evaluation, and challenges of feature selection,”IEEE Access, vol.10, pp.99595-99632, 2022.
[8] M.Ashfaq, N.Minallah, J.Frnda and L. Behan, “Multi-Modal Rigid Image Registration and Segmentation Using Multi-Stage Forward Path Regenerative Genetic Algorithm,” Symmetry, vol.14, no.8, pp.1506, 2022.
[9] C. Vensila and A. Boyed Wesley, “Multimodal biometrics authentication using extreme learning machine with feature reduction by adaptive particle swarm optimization,” The Visual Computer, vol.40, no.3, pp.1383-1394, 2024.
[10] X.Wang, H.Shangguan, F.Huang, S.Wu and W. Jia, “MEL: Efficient Multi-Task Evolutionary Learning for High-Dimensional Feature Selection,” IEEE Transactions on Knowledge and Data Engineering, 2024.
[11] L.Gao and L.Guan, “A Discriminant Information Theoretic Learning Framework for Multi-modal Feature Representation,” ACM Transactions on Intelligent Systems and Technology, vol.14, no.3, pp.1-24, 2023.
[12] P.Dhal and C. Azad, “A comprehensive survey on feature selection in the various fields of machine learning,” Applied Intelligence, vol.52, no.4, pp.4543-4581, 2022.
[13] S. K.Sharma, D. Muduli, A. Rath, S. Dash and G. Panda, “Discrete Ripplet-II Transform Feature Extraction and Metaheuristic-Optimized Feature Selection for Enhanced Glaucoma Detection in Fundus Images Using LS-SVM,” preprints, 2023.
[14] M. S. Zewudie, S. Xiong, X. Yu, X. Wu and M.A. Mehamed, “Adaptive feature selection for active trachoma image classification,” Knowledge-Based Systems, vol.294, pp.111764, 2024.
[15] Z. Zhao, H.Bai, J. Zhang, Y.Zhang, S.Xu et al., “Cddfuse: Correlation-driven dual-branch feature decomposition for multi-modality image fusion,” In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 5906-5916, 2023.
[16] D.Qu, H.Xiao, H. Chen and H. Li, “An improved differential evolution algorithm for multi-modal multi-objective optimization,” PeerJ Computer Science, vol.10, pp.e1839, 2024.
[17] M.A.Gil-Rios, M. A., Chalopin, C., Cruz-Aceves, I., Lopez-Hernandez, J. M., HernandezGonzalez and S.E.Solorio-Meza, “Automatic classification of coronary stenosis using feature selection and a hybrid evolutionary algorithm,” Axioms, vol.12, no.5, pp.462, 2023.
[18] Ö. F. Gürcan, U.Atıcı and O.F. Beyca, “A hybrid deep learning-metaheuristic model for diagnosis of diabetic retinopathy,” Gazi University Journal of Science, vol.36, no.2, pp.693-703, 2023.
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