An Integrated Approach towards Efficient Image Classification Using Deep CNN with Transfer Learning and PCA

  • Rahul Sharma Department of Computer Applications, Lovely Professional University, Jalandhar, India
  • Amar Singh Department of Computer Applications, Lovely Professional University, Jalandhar, India
Keywords: dimensionality reduction, feature extraction, image recognition, PCA, transfer learning

Abstract

In image processing, developing efficient, automated, and accurate techniques to classify images with varying intensity level, resolution, aspect ratio, orientation, contrast, sharpness, etc. is a challenging task. This study presents an integrated approach for image classification by employing transfer learning for feature selection and using principal component analysis (PCA) for feature reduction. The PCA algorithm is employed for reducing the dimensionality of the features extracted by the VGG16 model to obtain a handful of features for speeding up image reorganization. For multilayer perceptron classifiers, support vector machine (SVM) and random forest (RF) algorithms are used. The performance of the proposed approach is compared with other classifiers. The experimental results establish the supremacy of the VGG16-PCA-Multilayer perceptron model integrated approach and achieve a reorganization accuracy of 91.145%, 95.0%, 92.33%, and 98.59% on Fashion-MNIST dataset, ORL dataset of faces, corn leaf disease dataset, and rice leaf disease datasets, respectively.

References

R. Guha, A. H. Khan, P. K. Singh, R. Sarkar, and D. Bhattacharjee, “CGA: A New Feature Selection Model for Visual Human Action Recognition,” Neural Computing and Applications, vol. 33, no. 10, pp. 5267-5286, May 2021.

S. Ahuja, B. K. Panigrahi, N. Dey, V. Rajinikanth, and T. K. Gandhi, “Deep Transfer Learning-Based Automated Detection of COVID-19 from Lung CT Scan Slices,” Applied Intelligence, vol. 51, no. 1, pp. 571-585, January 2021.

J. Pardede, B. Sitohang, S. Akbar, and M. L. Khodra, “Implementation of Transfer Learning Using VGG16 on Fruit Ripeness Detection,” International Journal of Intelligent Systems and Applications, vol. 13, no. 2, pp. 52-61, 2021.

M. Q. Tran, M. K. Liu, and M. Elsisi, “Effective Multi-Sensor Data Fusion for Chatter Detection in Milling Process,” ISA Transactions, in press.

M. Q. Tran, M. Elsisi, and M. K. Liu, “Effective Feature Selection with Fuzzy Entropy and Similarity Classifier for Chatter Vibration Diagnosis,” Measurement, vol. 184, 109962, November 2021.

D. Hemavathi and H. Srimathi, “Effective Feature Selection Technique in an Integrated Environment Using Enhanced Principal Component Analysis,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 3, pp. 3679-3688, 2021.

S. Punitha, F. Al-Turjman, and T. Stephan, “An Automated Breast Cancer Diagnosis Using Feature Selection and Parameter Optimization in ANN,” Computers and Electrical Engineering, vol. 90, 106958, March 2021.

M. Elsisi, M. Q. Tran, K. Mahmoud, M. Lehtonen, and M. M. Darwish, “Deep Learning-Based Industry 4.0 and Internet of Things towards Effective Energy Management for Smart Buildings,” Sensors, vol. 21, no. 4, 1038, February 2021.

M. Q. Tran, M. Elsisi, K. Mahmoud, M. K. Liu, M. Lehtonen, and M. M. Darwish, “Experimental Setup for Online Fault Diagnosis of Induction Machines via Promising IoT and Machine Learning: Towards Industry 4.0 Empowerment,” IEEE Access, vol. 9, pp. 115429-115441, 2021.

M. Elsisi, M. Q. Tran, K. Mahmoud, D. E. A. Mansour, M. Lehtonen, and M. M. Darwish, “Towards Secured Online Monitoring for Digitalized GIS Against Cyber-Attacks Based on IoT and Machine Learning,” IEEE Access, vol. 9, pp. 78415-78427, 2021.

I. Ullah and Q. H. Mahmoud, “Design and Development of a Deep Learning-Based Model for Anomaly Detection in IoT Networks,” IEEE Access, vol. 9, pp. 103906-103926, 2021.

G. Delnevo, R. Girau, C. Ceccarini, and C. Prandi, “A Deep Learning and Social IoT approach for Plants Disease Prediction toward a Sustainable Agriculture,” IEEE Internet of Things Journal, in press.

T. Kaur and T. K. Gandhi, “Deep Convolutional Neural Networks with Transfer Learning for Automated Brain Image Classification,” Machine Vision and Applications, vol. 31, no. 3, 20, March 2020.

R. Pires De Lima and K. Marfurt, “Convolutional Neural Network for Remote-Sensing Scene Classification: Transfer Learning Analysis,” Remote Sensing, vol. 12, no. 1, 86, January 2020.

D. Xue, X. Zhou, C. Li, Y. Yao, M. M. Rahaman, J. Zhang, et al., “An Application of Transfer Learning and Ensemble Learning Techniques for Cervical Histopathology Image Classification,” IEEE Access, vol. 8, pp. 104603-104618, 2020.

M. Garcia-Dominguez, C. Dominguez, J. Heras, E. Mata, and V. Pascual, “FrImCla: A Framework for Image Classification Using Traditional and Transfer Learning Techniques,” IEEE Access, vol. 8, pp. 53443-53455, 2020.

M. Sert and E. Boyacı, “Sketch Recognition Using Transfer Learning,” Multimedia Tools and Applications, vol. 78, no. 12, pp. 17095-17112, June 2019.

S. L. Chen and L. W. Huang, “Using Deep Learning Technology to Realize the Automatic Control Program of Robot Arm Based on Hand Gesture Recognition,” International Journal of Engineering and Technology Innovation, vol. 11, no. 4, pp. 241-250, September 2021.

M. A. Khan, T. Akram, M. Sharif, K. Javed, M. Raza, and T. Saba, “An Automated System for Cucumber Leaf Diseased Spot Detection and Classification Using Improved Saliency Method and Deep Features Selection,” Multimedia Tools and Applications, vol. 79, no. 25, pp. 18627-18656, July 2020.

S. Kaur, H. Aggarwal, and R. Rani, “Diagnosis of Parkinson’s Disease Using Deep CNN with Transfer Learning and Data Augmentation,” Multimedia Tools and Applications, vol. 80, no. 7, pp. 10113-10139, March 2021.

Y. Li, J. Nie, and X. Chao, “Do We Really Need Deep CNN for Plant Diseases Identification?” Computers and Electronics in Agriculture, vol. 178, 105803, November 2020.

R. Sujatha, J. M. Chatterjee, N. Z. Jhanjhi, and S. N. Brohi, “Performance of Deep Learning vs Machine Learning in Plant Leaf Disease Detection,” Microprocessors and Microsystems, vol. 80, 103615, 2021.

S. K. Behera, A. K. Rath, and P. K. Sethy, “Maturity Status Classification of Papaya Fruits Based on Machine Learning and Transfer Learning Approach,” Information Processing in Agriculture, vol. 8, no. 2, pp. 244-250, June 2021.

H. J. Chiu, T. H. S. Li, and P. H. Kuo, “Breast Cancer-Detection System Using PCA, Multilayer Perceptron, Transfer Learning, and Support Vector Machine,” IEEE Access, vol. 8, pp. 204309-204324, 2020.

M. Loey, G. Manogaran, M. H. N. Taha, and N. E. M. Khalifa, “A Hybrid Deep Transfer Learning Model with Machine Learning Methods for Face Mask Detection in the Era of the COVID-19 Pandemic,” Measurement, vol. 167, 108288, January 2021.

R. Bhagwat and Y. Dandawat, “A Review on Advances in Automated Plant Disease Detection,” International Journal of Engineering and Technology Innovation, vol. 11, no. 4, pp. 251-264, September 2021.

S. Ren and C. Q. Li, “Robustness of Transfer Learning to Image Degradation,” Expert Systems with Applications, in press.

K. Weimann and T. O. Conrad, “Transfer Learning for ECG Classification,” Scientific Reports, vol. 11, no. 1, pp. 1-12, 5251, 2021.

J. Liu, F. Guo, H. Gao, M. Li, Y. Zhang, and H. Zhou, “Defect Detection of Injection Molding Products on Small Datasets Using Transfer Learning,” Journal of Manufacturing Processes, vol. 70, pp. 400-413, October 2021.

C. Li, Y. Yang, H. Liang, and B. Wu, “Transfer Learning for Establishment of Recognition of COVID-19 on CT Imaging Using Small-Sized Training Datasets,” Knowledge-Based Systems, vol. 218, 106849, 2021.

J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, “How Transferable are Features in Deep Neural Networks?” https://arxiv.org/pdf/1411.1792.pdf, November 06, 2014.

S. Mahajan, A. Raina, X. Z. Gao, and A. K. Pandit, “Plant Recognition Using Morphological Feature Extraction and Transfer Learning over SVM and Adaboost,” Symmetry, vol. 13, no. 2, 356, Februay 2021.

A. Singh, S. Kumar, A. Singh, and S. S. Walia, “Parallel 3-Parent Genetic Algorithm with Application to Routing in Wireless Mesh Networks,” Implementations and Applications of Machine Learning, vol. 782, pp. 1-27, 2020.

S. Dilmi and M. Ladjal, “A Novel Approach for Water Quality Classification Based on the Integration of Deep Learning and Feature Extraction Techniques,” Chemometrics and Intelligent Laboratory Systems, vol. 214, 104329, July 2021.

J. Wang, X. Sun, Q. Cheng, and Q. Cui, “An Innovative Random Forest-Based Nonlinear Ensemble Paradigm of Improved Feature Extraction and Deep Learning for Carbon Price Forecasting,” Science of the Total Environment, vol. 762, 143099, March 2021.

A. M. Ismael and A. Şengür, “Deep Learning Approaches for COVID-19 Detection Based on Chest X-Ray Images,” Expert Systems with Applications, vol. 164, 114054, Februaty 2021.

M. Amini, M. Pedram, A. Moradi, and M. Ouchani, “Diagnosis of Alzheimer’s Disease Severity with fMRI Images Using Robust Multitask Feature Extraction Method and Convolutional Neural Network (CNN),” Computational and Mathematical Methods in Medicine, vol. 2021, 5514839, 2021.

A. Z. da Costa, H. E. Figueroa, and J. A. Fracarolli, “Computer Vision Based Detection of External Defects on Tomatoes Using Deep Learning,” Biosystems Engineering, vol. 190, pp. 131-144, February 2020.

R. J. S. Raj, S. J. Shobana, I. V. Pustokhina, D. A. Pustokhin, D. Gupta, and K. Shankar, “Optimal Feature Selection-Based Medical Image Classification Using Deep Learning Model in Internet of Medical Things,” IEEE Access, vol. 8, pp. 58006-58017, 2020.

G. Bargshady, X. Zhou, R. C. Deo, J. Soar, F. Whittaker, and H. Wang, “Enhanced Deep Learning Algorithm Development to Detect Pain Intensity from Facial Expression Images,” Expert Systems with Applications, vol. 149, 113305, July 2020.

M. Bansal, M. Kumar, M. Sachdeva, and A. Mittal, “Transfer Learning for Image Classification Using VGG19: Caltech-101 Image Data Set,” Journal of Ambient Intelligence and Humanized Computing, in press.

K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” https://arxiv.org/pdf/1409.1556.pdf, April 10, 2015.

K. Pearson, “LIII. On Lines and Planes of Closest Fit to Systems of Points in Space,” The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, vol. 2, no. 11, pp. 559-572, 1901.

M. Toğaçar, Z. Cömert, and B. Ergen, “Classification of Brain MRI Using Hyper Column Technique with Convolutional Neural Network and Feature Selection Method,” Expert Systems with Applications, vol. 149, 113274, July 2020.

L. Yao, Z. Fang, Y. Xiao, J. Hou, and Z. Fu, “An Intelligent Fault Diagnosis Method for Lithium Battery Systems Based on Grid Search Support Vector Machine,” Energy, vol. 214, 118866, January 2021.

Published
2021-11-15
How to Cite
[1]
R. Sharma and A. Singh, “An Integrated Approach towards Efficient Image Classification Using Deep CNN with Transfer Learning and PCA”, Adv. technol. innov., Nov. 2021.
Section
Articles