Abstract
Classifying images is one of the most common methods adopted in the current processes and the current development of scientific experiments that provide for the topics of change and development in the field of classification and make them into groups, perhaps small or large, and according to the size of the data used and stored in memory, as is the case in our work. In this study, 2749 microscopic images of a type of algae were used to observe the nature of its shape, accuracy, and quality of similarity between them; it is considered one of the essential classifications and may need some accuracy in its classification, so it needs experts. The program Mat-lab was used to obtain good results in the analysis and collection of the data used in the data and the nature of the work. We have used a developed program that is currently popular in classification processes to get results quickly, which is Convolution Neural Network (CNN), which is part of machine learning through which can collect data and the images are easily and quickly the user, which is a supported language in network analyzer and using quantitative algorithms for neural network and alexnet. Finally, using CNN's deep learning method to show that there are changes in accuracy, up and down, in the data used may be a challenging area to know the causes and to delve into the results that may lead to a good analysis.
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Hawezi, R.S. Image Classification of Algal Species Applied Deep Learning Algorithms. Wireless Pers Commun (2023). https://doi.org/10.1007/s11277-023-10488-z
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DOI: https://doi.org/10.1007/s11277-023-10488-z