Comparative Analysis for Improving Accuracy of Image Classification Using Deep Learning Architectures

Image classification is a classic problem in areas pertaining to Computer Vision, Image Processing, and Machine Learning. This paper aims to compare the various Deep Learning Architectures to improve the accuracy of Image Classification to select the best Deep Learning Architecture by implementing and testing various Deep Learning Architectures in combination with Dense Neural Networks. This comparative study helps to improve the accuracy of image separation in both training and testing databases. For targeted training and testing, 3000 training images and 1000 test images were used. The result of the Deep Learning-based classification of images using the platform as Google Colab showed how accurate classification was done by comparing various deep learning architectures.