DOI : https://doi.org/10.62527/ijasce.5.2.132

Impact the Classes’ number on the convolutional neural networks performance for image classification

Amna Kadhim Ali (1), Abdulhussein Mohsin Abdullah (2), Sabreen Fawzi Raheem (3)
(1) Basrah university/ Iraq
(2) Computer Technology Engineering Department, Alkunooze University College, Basrah, Iraq
(3) Basrah technical Institute, Southern Technical University, Basrah, Iraq
Fulltext View | Download
How to cite (IJASEIT) :
Ali, A. K., Abdullah, A. M., & Raheem, S. F. (2023). Impact the Classes’ number on the convolutional neural networks performance for image classification. International Journal of Advanced Science Computing and Engineering, 5(2), 119–128. https://doi.org/10.62527/ijasce.5.2.132
Citation Format :

Deep learning was developed as a realistic artificial intelligence technique that takes in numerous layers of information and produces the best results in various classes. Deep learning has demonstrated excellent performance in several areas, particularly picture grouping, division, and recognition. The convolutional neural network (CNN) is one of the algorithms that relies on deep learning in its work. It has proven its effectiveness in classifying images with high efficiency in medical images and their diagnoses, face recognition, and other different fields. In this paper, the focus was on images to alert new researchers to their effects on the performance of CNN in terms of the number of classes that existed within the database, in addition to the impact of incorrect classification of images by the source on the classification result and the necessity of adopting reliable and correct sources of data to avoid inaccurate results. A group of face images has been used, and three experiments on them were conducted using all existing classes with reduction. The results showed a significant improvement in the performance of the algorithm whenever the number of classes was reduced. The best result was when only two classes were chosen for classification, reaching a validation accuracy of 85%.

C. Zuo et al., "Deep learning in optical metrology: a review,", Light Sci Appl, vol. 11, no. 1, p. 39, 2022, doi: 10.1038/s41377-022-00714-x.

M. Z. Alom et al., "A state-of-the-art survey on deep learning theory and architectures,", Electronics, vol. 8, no. 3, p. 292, 2019, doi: 10.3390/electronics8030292.

T. Kattenborn, J. Leitloff, F. Schiefer, S. J. I. j. o. p. Hinz, and r. sensing, "Review on Convolutional Neural Networks (CNN) in vegetation remote sensing,", J ISPRS, vol. 173, pp. 24-49, 2021, doi: 10.1016/j.isprsjprs.2020.12.010.

J. Gu et al., "Recent advances in convolutional neural networks,", Pattern Recognition, vol. 77, pp. 354-377, 2018, doi: 10.1016/j.patcog.2017.10.013.

P. T. Q. Anh, D. Q. Thuyet, Y. J. P. B. Kobayashi, and Technology, "Image classification of root-trimmed garlic using multi-label and multi-class classification with deep convolutional neural network,", Postharvest Biology and Technology, vol. 190, p. 111956, 2022, doi: 10.1016/j.postharvbio.2022.111956.

M. M. Yusro, R. Ali, and M. S. J. B. S. J. Hitam, "Comparison of Faster R-CNN and YOLOv5 for Overlapping Objects Recognition,", BSJ, vol. 20, no. 3, pp. 0893-0893, 2023, doi: 10.21123/bsj.2022.7243 .

A. K. Sharma et al., "Dermatologist-level classification of skin cancer using cascaded ensembling of convolutional neural network and handcrafted features based deep neural network,", IEEE Access, vol. 10, pp. 17920-17932, 2022, doi: 10.1109/ACCESS.2022.3149824.

F. S. Hanoon, A. H. H. J. I. J. o. E. Alasadi, and C. Engineering, "A modified residual network for detection and classification of Alzheimer’s disease,", IJECE, vol. 12, no. 4, pp. 4400-4407, 2022, doi: 10.11591/ijece.v12i4.pp4400-4407.

H. S. Das, A. Das, A. Neog, S. Mallik, K. Bora, and Z. J. F. i. G. Zhao, "Breast cancer detection: Shallow convolutional neural network against deep convolutional neural networks based approach,", Frontiers, vol. 13, p. 1097207, 2023.

H. O. Ikromovich and B. B. J. O. A. R. Mamatkulovich, "FACIAL RECOGNITION USING TRANSFER LEARNING IN THE DEEP CNNâ€, Open Access Repository, vol. 4, no. 3, pp. 502-507, 2023, doi: 10.17605/OSF.IO/NRMK2.

Y. Cao et al., "MBANet: A 3D convolutional neural network with multi-branch attention for brain tumor segmentation from MRI images,", Biomedical Signal Processing and Control, vol. 80, p. 104296, 2023, doi: 10.1016/j.bspc.2022.104296.

J. Alfred Daniel et al., "Fully convolutional neural networks for LIDAR–camera fusion for pedestrian detection in autonomous vehicle,", Multimedia Tools and Applications, pp. 1-24, 2023.

M. Ahmadi, A. Sharifi, M. Jafarian Fard, and N. J. I. j. o. n. Soleimani, "Detection of brain lesion location in MRI images using convolutional neural network and robust PCA,", International Journal of Neuroscience, vol. 133, no. 1, pp. 55-66, 2023, doi: 10.1080/00207454.2021.1883602.

H. Chen, L. Wu, J. Chen, W. Lu, J. J. I. P. Ding, and Management, "A comparative study of automated legal text classification using random forests and deep learning,", Information Processing & Management, vol. 59, no. 2, p. 102798, 2022, doi: 10.1016/j.ipm.2021.102798.

S. Soni, S. S. Chouhan, and S. S. J. A. I. Rathore, "TextConvoNet: A convolutional neural network based architecture for text classification,", Applied Intelligence, vol. 53, no. 11, pp. 14249-14268, 2023.

V. R. J. I. J. o. C. Allugunti, Programming and D. Management, "A machine learning model for skin disease classification using convolution neural network,", International Journal of Computing, Programming and Database Management, vol. 3, no. 1, pp. 141-147, 2022.

A. S. Musallam, A. S. Sherif, and M. K. J. I. a. Hussein, "A new convolutional neural network architecture for automatic detection of brain tumors in magnetic resonance imaging images,", IEEE Access, vol. 10, pp. 2775-2782, 2022, doi: 10.1109/ACCESS.2022.3140289 .

J.-H. Kim, B.-G. Kim, P. P. Roy, and D.-M. J. I. a. Jeong, "Efficient facial expression recognition algorithm based on hierarchical deep neural network structure,", IEEE access, vol. 7, pp. 41273-41285, 2019, doi: 10.1109/ACCESS.2019.2907327.

N. M. A.-M. M. Al and R. S. J. I. Khudeyer, "ResNet-34/DR: a residual convolutional neural network for the diagnosis of diabetic retinopathy,", Informatica, vol. 45, no. 7, 2021.

A. Mollahosseini, B. Hasani, and M. H. J. I. T. o. A. C. Mahoor, "Affectnet: A database for facial expression, valence, and arousal computing in the wild,", IEEE Transactions on Affective Computing, vol. 10, no. 1, pp. 18-31, 2017, doi: 10.1109/TAFFC.2017.2740923 .

A. M. Hasan, A. F. Qasim, H. A. Jalab, and R. W. J. B. S. J. Ibrahim, "Breast cancer MRI classification based on fractional entropy image enhancement and deep feature extraction,", BSJ, vol. 20, no. 1, pp. 0221-0221, 2023, doi: 10.21123/bsj.2022.6782.

R. Sarki et al., "Image preprocessing in classification and identification of diabetic eye diseases,", Data Science Engineering, vol. 6, no. 4, pp. 455-471, 2021, doi: 10.1007/s41019-021-00167-z.

D. G. J. J. o. I. I. P. Ranganathan, "A study to find facts behind preprocessing on deep learning algorithms,", Journal of Innovative Image Processing, vol. 3, no. 1, pp. 66-74, 2021, doi: 10.36548/jiip.2021.1.006.

W. Samek, G. Montavon, S. Lapuschkin, C. J. Anders, and K.-R. J. P. o. t. I. Müller, "Explaining deep neural networks and beyond: A review of methods and applications,", IEEE, vol. 109, no. 3, pp. 247-278, 2021, doi: 10.1109/JPROC.2021.3060483 .

Z. Li, F. Liu, W. Yang, S. Peng, J. J. I. t. o. n. n. Zhou, and l. systems, "A survey of convolutional neural networks: analysis, applications, and prospects,", IEEE, vol. 33, no. 12, pp. 6999 – 7019, 2021, doi: 10.1109/TNNLS.2021.3084827 .

S. Cong and Y. J. A. I. R. Zhou, "A review of convolutional neural network architectures and their optimizations,", Artificial Intelligence Review, vol. 56, no. 3, pp. 1905-1969, 2023.

A. Khan, A. Sohail, U. Zahoora, and A. S. J. A. i. r. Qureshi, "A survey of the recent architectures of deep convolutional neural networks,", Artificial Intelligence Review, vol. 53, pp. 5455-5516, 2020.

D. Negrov, I. Karandashev, V. Shakirov, Y. Matveyev, W. Dunin-Barkowski, and A. J. N. Zenkevich, "An approximate backpropagation learning rule for memristor based neural networks using synaptic plasticity,", Neurocomputing, vol. 237, pp. 193-199, 2017, doi: 10.1016/j.neucom.2016.10.061.

S. Kiranyaz et al., "1D convolutional neural networks and applications: A survey,", Mechanical Systems and Signal Processing, vol. 151, p. 107398, 2021, doi: 10.1016/j.ymssp.2020.107398.

V. H. Phung and E. J. J. A. S. Rhee, "A high-accuracy model average ensemble of convolutional neural networks for classification of cloud image patches on small datasets,", Applied Sciences, vol. 9, no. 21, p. 4500, 2019, doi: 10.3390/app9214500.