Advancing Nuclei Detection in Drug Discovery: A Hybrid CNN+LSTM Approach with Curved Gaussian Distribution

In the field of drug discovery, the automated detection and analysis of cell nuclei within biological images play a pivotal role in understanding cellular responses to potential drug compounds. Leveraging the capabilities of DL techniques, this research presents a novel hybrid approach for nuclei detection, combining Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks, enhanced by a curved Gaussian distribution model. The objective is to achieve accurate and efficient nuclei detection to facilitate drug discovery processes. Utilizing the Broad Bioimage Benchmark Collection (BBBC) datasets, this study proposes a multi-stage detection pipeline. In the initial stage, a CNN is employed to capture intricate spatial features within the images, enabling the precise localization of nuclei. Subsequently, an LSTM network is introduced to exploit temporal dependencies in time-lapse microscopy images, aiding in tracking nuclei across consecutive frames. To further improve detection accuracy, a curved Gaussian distribution model is integrated to refine the predictions, considering the inherent curvature of nuclei boundaries. The proposed hybrid approach demonstrates exceptional performance, achieving an impressive accuracy of 98.87% on BBBC datasets. The combination of CNN and LSTM networks capitalizes on the complementary strengths of both architectures, resulting in accurate detection and tracking of nuclei over time. The incorporation of the curved Gaussian distribution model enhances the precision of localization, particularly at challenging boundaries. These results underscore the potential of DL techniques for nuclei detection in drug discovery and contribute to advancing the understanding of cellular dynamics under the influence of various drug compounds. This research not only provides a robust solution for nuclei detection but also paves the way for further innovations in automating cellular analysis within drug discovery pipelines. The hybrid approach's high accuracy and adaptability to diverse biological imaging scenarios underscore its potential to accelerate drug development processes and streamline the identification of potential therapeutic candidates.