Analysis of Image Based Classification Using Machine Learning Techniques

This research study about image classification by using the deep neural network (DNN) or also known as Deep Learning by using framework TensorFlow. Python is used as a programming language because it comes together with TensorFlow framework. The input data mainly focuses in Fruit category which there are different types of Fruit that have been used in this paper. Deep neural network (DNN) has been choosing as the best option for the training process because it produced a high percentage of accuracy. Results are discussed in terms of the accuracy of the image classification in percentage. apple get 90.585% and same goes to another type of fruit where the average of the result is up to 90% and above. Image classification serves as the foundation for many computer vision tasks, enabling machines to interpret and understand visual data. By automatically categorizing images into predefined classes or labels, machine learning models facilitate efficient decision-making processes and enable automation in various real-world scenarios. Applications range from medical image diagnosis and surveillance systems to content recommendation algorithms and image search engines. Machine learning approaches to image classification typically involve the use of convolutional neural networks (CNNs), which have demonstrated remarkable performance in extracting relevant features from images and learning complex patterns. CNNs leverage hierarchical layers of convolutional filters to extract increasingly abstract representations of images, followed by fully connected layers for classification. Transfer learning, data augmentation, and ensemble methods are commonly employed techniques to enhance the performance and robustness of image classification models. The future of image classification using machine learning holds promise for advancements in various areas.