Convolutional Neural Network Based Image Classification


  • Archana Kumari M.Tech[Industrial Electronics], Mohan Garden, Uttam Nagar , New Delhi - 110059, India


CK , FER, Emotion Recognition, CNN models, AlexNet, Res-Net-50, SARS-CoV-2 dataset, Grad-CAM, SVM, Fuzzy metric space, Limit theorems, Cauchy sequence, fuzzy diameter, strong and weak convergence, Random set, Fuzzy random variable., Analytic Hierarchy Process


This paper presents the study of Convolutional Neural Network based Image Classification. In this study, different convolutional neural network model is implemented for image classification. There are two application which is performed in this namely, emotion detection from facial emotion recognition system and covid-19 detection from lung CT-scan. In this research work, AlexNet and ResNet-50 convolutional neural network models are used for comparison and evaluation is done based on its training accuracy and testing accuracy, confusion matrix and Area under the curve (AUC) of ROC graph. All the experiments are performed on MATLAB software. In the First application of CNN model is implemented to detect emotions from facial expressions and SVM classifier is used for classification of each emotion among eight facial expressions namely, surprise, contempt, happiness, sadness, fear, anger, disgust, and neutral. The following proposed work is carried out on CK+ datasets to determine the recognition. After performing on both CNN models accuracy of both the models are compared. The result shows that ResNet-50 model achieved the best accuracy of 97.32% which is better than 90.55% which got on AlexNet model. In Second application of CNN model is to detect covid-19 from lung CT-scans. The highest performing model, the ResNet-50 on a SARS-CoV-2 CT-scan dataset achieved an accuracy of 95.72% which is maximum than 85.50% which achieved on AlexNet Model. Gradient-Weighted Class Activation Mapping (Grad-CAM) is also used to display infected area in the lungs. After performing this experiment, the final results shows that the ResNet-50 model, performs much better as compared to AlexNet model.


Pranav E., Suraj Kamal, Satheesh Chandran C., Supriya M.H..: Facial Emotion Recognition Using Deep Convolutional Neural Network.6th International Conference on Advanced Computing & Communication Systems (ICACCS), pp. 317-320.IEEE (2020).

Sai Yeshwanth Chaganti, Ipseeta Nanda, Koteswara Rao Pandi, Tavva GNRSN Prudhvith, Niraj Kumar.: Image Classification using SVM and CNN, International Conference on Computer Science, Engineering and Applications (ICCSEA), pp. 1-5. IEEE (2020).

Balaji Balasubramanian, Pranshu Diwan.: Analysis of Facial Emotion Recognition. 3rd International Conference on Trends in Electronics and Informatics, pp.945-949. IEEE (2019).

Guolu Cao, Yuliang Ma*, Xiaofei Meng, Yunyuan Gao, Ming Meng,” Emotion Recognition Based On CNN”, 2019 Chinese Control Conference (CCC), Guangzhou, China, 2019, pp. 8627-8630.

D. F. Eljamassi and A. Y. Maghari, "COVID-19 Detection from Chest X-ray Scans using Machine Learning," 2020 International Conference on Promising Electronic Technologies (ICPET), 2020, pp. 1-4.

P. Hartono, C. R. Luhur, C. A. Indriyani, C. R. Wijaya, N. N. Qomariyah and A. A. Purwita,"Evaluating Deep Learning for CT Scan COVID-19 Automatic Detection," 2021 International Conference on ICT for Smart Society (ICISS), 2021, pp. 1-7.

Aseel Qassim Abdul Ameer, Raghad Falih Mohammed, “Covid-19 detection using CT scan based on gray level Co-Occurrence matrix”, Materials Today: Proceedings,

Saood A, Hatem I, “COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet”, BMC Med Imaging, 2021 Feb,

E. Soares, P. Angelov, S. Biaso, M. H. Froes, and D. K. Abe, “SARS-CoV-2 CT-scan Dataset: A Large Dataset of Real Patients CT scans for SARS-CoV-2 Identification”, medRxiv, New Haven, CT, USA, 2020.

Nahla Nour, Mohammed Elhebir and Serestina Viriri,” Face Expression Recognition Using Convolution Neural Network (CNN) Models”, International Journal of Grid Computing & Applications (IJGCA) Vol.11, No.1/2/3/4. December 2020.

Y. Li, J. Zeng, S. Shan and X. Chen, "Occlusion Aware Facial Expression Recognition Using CNN With Attention Mechanism," in IEEE Transactions on Image Processing, vol. 28, no. 5, pp. 2439-2450, May 2019, doi: 10.1109/TIP.2018.2886767.

Aayush Bhardwaj, Ankit Gupta, Pallav Jain, Asha Rani, Jyoti Yadav.: Classification of human emotions from EEG signals using SVM and LDA Classifiers. 2nd International Conference on Signal Processing and Integrated Networks (SPIN), pp. 180-185.IEEE(2015).

G., Oztel, I., Kazan, S. et al.,"Facial expression recognition for monitoring neurological disorders based on convolutional neural network”, Multimed Tools Appl 78, 31581–31603 (2019).

Wafa Mellouk, Wahida Handouzi,” Facial emotion recognition using deep learning: review and insights”, Procedia Computer Science, Volume 175, 2020, Pages 689-694.

C. Ruoxuan, L. Minyi, and L. Manhua,” Facial Expression Recognition Based on Ensemble of Multiple CNNs”, Lecture Notes in Computer Science (), vol 9967, pp. 511-578, Springer International Publishing AG (2016).

Deepak Kumar Jain, Pourya Shamsolmoali, Paramjit Sehdev, “Extended deep neural network for facial emotion recognition”, Pattern Recognition Letters, Volume 120, 2019, Pages 69-74,

D. H. Kim, W. J. Baddar, J. Jang and Y. M. Ro, "Multi-Objective Based Spatio-Temporal Feature Representation Learning Robust to Expression Intensity Variations for Facial Expression Recognition," in IEEE Transactions on Affective Computing, vol. 10, no. 2, pp. 223-236, 1 April-June 2019, doi: 10.1109/TAFFC.2017.2695999.

André Teixeira Lopes, Edilson de Aguiar, Alberto F. De Souza, Thiago Oliveira-Santos, “Facial expression recognition with Convolutional Neural Networks: Coping with few data and the training sample order”, Pattern Recognition, Volume 61, 2017, Pages 610-628.

Osama Shahid, Mohammad Nasajpour, Seyedamin Pouriyeh, Reza M. Parizi, Meng Han, Maria Valero, Fangyu Li, Mohammed Aledhari, Quan Z. Sheng, “Machine learning research Towards combating COVID-19: Virus detection, spread prevention, and medical assistance”, Journal of Biomedical Informatics, Volume 117, 2021,

M. J. Horry, S. Chakraborty, M. Paul, A. Ulhaq, B. Pradhan, M. Saha, and N. Shukla, “Covid-19 detection through transfer learning using multimodal imaging data,” IEEE Access, vol. 8, pp. 149, 2020.

H. X. Bai, R. Wang, Z. Xiong, B. Hsieh, K. Chang, K. Halsey, T. M. L. Tran, J. W. Choi, D.-C. Wang, L.- B. Shi, J. Mei, X.-L. Jiang, I. Pan, Q.- H. Zeng, P.-F. Hu, Y.-H. Li, F.-X. Fu, R. Y. Huang, R. Sebro, Q.-Z. Yu, M. K. Atalay, and W.-H. Liao, “Artificial Intelligence Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Origin at Chest CT,” Radiology, vol. 296, no. 3, pp. E156– E165, 2020.

S. Wang, Y. Zha, W. Li, Q. Wu, X. Li, M. Niu, M. Wang, X. Qiu, H. Li, H. Yu, W. Gong, Y. Bai, L. Li, Y. Zhu, L. Wang, and J. Tian, “A fully automatic deep learning system for covid-19 diagnostic and prognostic analysis,” European Respiratory Journal, vol. 56, no. 2, 2020.

L. Sun, Z. Mo, F. Yan, L. Xia, F. Shan, Z. Ding, B. Song, W. Gao, W. Shao, F. Shi, H. Yuan, H. Jiang, D. Wu, Y. Wei, Y. Gao, H. Sui, D. Zhang, and D. Shen, “Adaptive feature selection guided deep forest for covid-19 classification with chest ct,” IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 10, pp. 2798–2805, 2020.

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, 2020.

Garain, A. Basu, F. Giampaolo, J. D. Velasquez, and R. Sarkar, “Detection of COVID 19 from CT scan images: A spiking neural network-based approach,” Neural Computing and Applications, vol. 33,19 (2021).

V. Shah, R. Keniya, A. Shridharani, M. Punjabi, J. Shah, and N. Mehendale, “Diagnosis of COVID-19 using CT scan images and deep learning techniques,” Emergency Radiology, vol. 28, no. 3, pp. 497–505, 2021.

M. Maftouni, A. C. C. Law, B. Shen, Y. Zhou, N. Ayoobi Yazdi, and Z. Kong, “A robust ensemble-deep learning model for covid-19 diagnosis based on an integrated ct scan images database,” June 2021.

Ahmad, A., Garhwal, S., Ray, S.K. et al,” The Number of Confirmed Cases of Covid-19 by using Machine Learning: Methods and Challenges”, Arch Computat Methods Eng 28, 2645–2653 (2021).

Apostolopoulos, I.D., Mpesiana, T.A,” Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks”, Phys Eng Sci Med 43, 635–640 (2020)

Selvaraju, R. R., M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra. "Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization." In IEEE International Conference on Computer Vision (ICCV), 2017, pp. 618–626. Available at Grad-CAM on the Computer Vision Foundation Open Access website.




How to Cite

Archana Kumari. (2022). Convolutional Neural Network Based Image Classification. International Journal of Sciences: Basic and Applied Research (IJSBAR), 65(1), 67–95. Retrieved from