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The COVID-19 Images Classification by MobileNetV3 and Enhanced Sine Cosine Metaheuristics

2022; Springer International Publishing; Linguagem: Inglês

10.1007/978-981-19-2069-1_65

2367-4512

Miodrag Živković, Aleksandar Petrović, Nebojša Bačanin, Stefan Milosevic, Vasilije Veljic, Ana Vesić,

Image Processing Techniques and Applications

The research proposed in this paper shows the application of the MobileNetV3 and improved sine cosine algorithm utilized for solving problems of optimization, thus including COVID-19 image classification. The algorithm that is the subject of optimization is a more recent solution in the field of population-based algorithms. The algorithm initializes random solutions upon each new iteration, and these solutions are candidates for the optimal solution. Solutions should generally tend toward the best solution or the opposite from it, depending in the problem to be solved. Such operations are performed by utilizing the sine and cosine functions in a mathematical adaption for the problem of optimization. The proposed improved version of the algorithm has been used to address the COVID-19 image classification problem. The problem of this case is a very challenging one and the study verifies and demonstrates the proposed improvements in terms of algorithm’s performance. The obtained findings from the proposed improved algorithm suggest superior performances in contrast to other methods included in the research. The proposed algorithm outperforms any other metaheuristics, in particular in terms of the feature numbers and classification accuracy.