Dear Md Foysal Ahmed ,

The article focuses on the concepts of Cell Image Segmentation (CIS) and the gradual introduction of cell counting. Motivated by the rapid development of Machine learning (ML) methods, which is carried out in this investigation. ML is evolving from theory to practical applications, with deep neural network models extensively used in academia and business for various applications, including image counting and natural language processing. These advancements can greatly influence medical imaging technologies, data processing, diagnostics, and healthcare in general. Main objectives of the research are to provide an overview of biological cell counting methods in microscopic images and to explore deep learning (DL)-based image segmentation approaches. The study expertly describes current trends, cutting-edge learning technologies, and platforms utilized for DL approaches. Cell counting is one of the most researched and challenging subjects in computer vision systems. Academics are increasingly interested in this area due to its real-time applications in biology, biochemistry, medical diagnostics, computer vision-based cell tracking systems for large populations, and stem cell manufacturing.

Article A Systematic Survey on Biological Cell Image Segmentation an...

Regards,

Shafagat

Dear Md Foysal Ahmed ,

Cell count and segmentation are essential in experimental drug effect analysis, providing both quantitative and qualitative insights. Cell counting helps assess changes in cell proliferation, growth, or death, offering a clear measure of a drug's efficacy or toxicity. Segmentation enables detailed visualization of individual cells, allowing researchers to study drug-induced changes in cell morphology, structure, and specific subcellular components. Additionally, segmentation aids in tracking cell populations and behaviors, such as movement and differentiation. Together, these techniques offer a comprehensive view of drug impacts on cellular dynamics, helping to optimize drug effectiveness, dosage, and identify potential side effects.

Here are two useful research papers on the application of deep learning in analyzing drug effects, including drug–target interactions (DTI) and drug–drug interaction (DDI) predictions:

1. “DeepPurpose: A deep learning library for drug–target interaction prediction” (Huang et al., 2020) .

2. “A Computational-Based Method for Predicting Drug–Target Interactions Using a Stacked Autoencoder Deep Neural Network” (Wang et al., 2018) .

Cell count and segmentation are crucial in experimental drug effect analysis as they provide detailed information on cellular responses to treatments. Accurate cell counting helps assess drug efficacy by quantifying changes in cell populations, while segmentation enables the analysis of cell morphology and spatial distribution. Together, these methods can reveal insights into drug impact on cell growth, health, and behavior, guiding therapeutic development and optimization.