• Gopi Krishna
• Gundreddy Tharun Reddy
• Dr. Ch. Subba Lakshmi

The integration of machine learning algorithms into medical image analysis has revolutionized the approach to automated cell counting, offering unprecedented accuracy, efficiency, and applicability across various medical imaging modalities. This paper explores the forefront of automated cell counting techniques, emphasizing the Manifold-Regularized Regression Network (MRRN), Hybrid Deep Autoencoder (HDA) with Curvature Gaussian, and U-Net architectures. Each method's unique strengths and applicability to specific challenges in medical image analysis are dissected, ranging from handling high-density cell populations and variability in cell shapes and colors with MRRN, achieving precise detection of specific cell types with HDA, to the versatile and user-friendly U-Net for broad cell counting and segmentation tasks. Through a comprehensive review of recent advancements, this study highlights the critical role of deep learning in enhancing the precision and generalizability of cell counting techniques, thereby facilitating accurate diagnostics and research in biomedical imaging. The discussion extends to the algorithms' performance metrics, application scenarios, and their contribution to overcoming traditional hurdles in automated cell counting. This paper aims to guide researchers and practitioners in selecting appropriate machine learning-based strategies for specific medical image analysis tasks, fostering further innovations and applications in the field.

Automated Cell Counting, Machine Learning, Medical Image Analysis, Deep Learning, Manifold-Regularized Regression Network, Hybrid Deep Autoencoder, U-Net, Cell Localization, Biomedical Imaging, Histopathological Image Segmentation.

"Comparative Analysis of Automated Cell Counting Algorithms in Medical Image Analysis: A Study of MRRN, HDA with Curvature Gaussian, and U-Net", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.a276-a282, April-2024, Available :http://www.jetir.org/papers/JETIR2404037.pdf

"Comparative Analysis of Automated Cell Counting Algorithms in Medical Image Analysis: A Study of MRRN, HDA with Curvature Gaussian, and U-Net", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppa276-a282, April-2024, Available at : http://www.jetir.org/papers/JETIR2404037.pdf