Deep Convolution Neural Networks for Image Classification

Deep learning is a highly active area of research in machine learning community. Deep Convolutional Neural Networks (DCNNs) present a machine learning tool that enables the computer to learn from image samples and extract internal representations or properties underlying grouping or categories of the images. DCNNs have been used successfully for image classification, object recognition, image segmentation, and image retrieval tasks. DCNN models such as Alex Net, VGG Net, and Google Net have been used to classify large dataset having millions of images into thousand classes. In this paper, we present a brief review of DCNNs and results of our experiment. We have implemented Alex Net on Dell Pentium processor using MATLAB deep learning toolbox. We have classified three image datasets. The first dataset contains four hundred images of two types of animals that was classified with 99.1 percent accuracy. The second dataset contains four thousand images of five types of flowers that was classified with 86.64 percent accuracy. In the first and second dataset seventy percent randomly chosen samples from each class were used for training. The third dataset contains forty images of stained pleura tissues from rat-lungs are classified into two classes with 75 percent accuracy. In this data set eighty percent randomly chosen samples were used in training the model.

@article{Kulkarni2022,
  title     = {Deep Convolution Neural Networks for Image Classification},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {6},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Arun D. Kulkarni},
  doi       = {10.14569/IJACSA.2022.0130603},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130603}
}