Image classification with deconvolution operation and augmentation

Several image classification approaches have been evolved over the years utilizing convolutional neural network (CNN). In convolution operation of CNN, the shifting of kernels to overlapping regions of the image learns redundant data as the images are strongly correlated in reality. 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.