With the increasing complexity of neural networks (NNs) in critical applications, ensuring their reliability and robustness is essential. Traditional software testing methods fall short when applied to NNs, necessitating the development of new strategies tailored to their unique architecture. This paper introduces a comprehensive assessment of advanced structural coverage metrics designed for NN testing, building upon the limitations of existing approaches. We explore various coverage metrics, such as neuron coverage, layer coverage, and path coverage, and evaluate their effectiveness in capturing the functional behavior of NNs under diverse test scenarios. Additionally, we propose a hybrid metric that integrates multiple coverage criteria to achieve higher fault detection capabilities. Using empirical studies on popular deep learning models, we demonstrate how these metrics can improve testing rigor, leading to the identification of subtle errors and enhancing overall network reliability. The study highlights the need for specialized coverage metrics and offers insights into their implementation, thus contributing to the growing field of NN testing methodologies.