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The redundant data make the neural network training a challenging task. Again, Deep Learning methods evaluated on small dataset yields degraded performance. To deal with these issues, a proposal is made in this paper that uses deconvolution operation to minimize correlations from images and data augmentation technique to increase the size of datasets. Plant Village, Tomato, and Covid-19 datasets were used for evaluating the performance of the proposed method. 70% of the datasets were used for training, 10% for validation, and 20% for testing purposes. The CIFAR10, MNIST, and Mini-ImageNet datasets were also considered for performance evaluation. The proposed method performed better than other existing methods in terms of classification accuracy.<\/jats:p>","DOI":"10.1093\/comjnl\/bxae099","type":"journal-article","created":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T03:11:53Z","timestamp":1729912313000},"page":"135-144","source":"Crossref","is-referenced-by-count":1,"title":["Image classification with deconvolution operation and augmentation"],"prefix":"10.1093","volume":"68","author":[{"given":"Nayan","family":"Kumar Sarkar","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering , North Eastern Regional Institute of Science and Technology, Nirjuli, Arunachal Pradesh 791109,","place":["India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Moirangthem","family":"Marjit Singh","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering , North Eastern Regional Institute of Science and Technology, Nirjuli, Arunachal Pradesh 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