A CNN-GA Hybrid Approach for Efficient Analysis of Size and Shape of Nanoparticle

Addressing the challenge of manually analyzing nanoparticle characteristics, such as size and shape, researchers have increasingly turned to deep learning methodologies. This research paper introduces a novel approach, a synergistic combination of CNN-GA architecture, to efficiently analyze the physical properties of nanoparticles. The integration of CNN for feature extraction from image data and GA for fine-tuning hyperparameters and attributes proves to be a powerful tool. Unlike the traditional manual design of CNN architectures, this semi-automatic approach allows researchers to design robust architectures without necessitating in-depth CNN expertise, facilitating the achievement of benchmark results in nanoparticle characterization.