Browsing by Author "Sautami Basu"
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PublicationArticle Development of 3D Intelligent Quantitative Phase Microscope for Sickle Cells Screening(John Wiley and Sons Inc, 2025) Sautami Basu; Gyanendra Singh; Ravinder Agarwal; Vishal SrivastavaSickle cell disease (SCD) is a genetic blood disorder causing red blood cells to deform into a sickle shape, often leading to misdiagnosis. Early detection is crucial, but traditional screening is slow and labor-intensive. This paper introduces an intelligent microscope system for automated SCD screening, reducing manual intervention. The system uses an interferometric method to capture high-resolution 3D phase images, combined with a deep learning-based UNET model for semantic segmentation of sickle and healthy cells. Various machine-learning models classify RBCs, with the Gradient boosting model achieving 94.9% accuracy. The system is scalable, user-friendly, and well suited for resource-limited settings, offering a faster, more reliable diagnostic tool. This innovation not only improves SCD detection but also sets the stage for AI-driven haematological diagnostics. Future advancements will enhance system robustness and undergo extensive clinical validation. © 2025 Wiley-VCH GmbH.PublicationConference Paper Screening of Sickle Cells Using Intelligent Microscope(Institute of Electrical and Electronics Engineers Inc., 2023) Vishal Srivastava; Sautami Basu; Gyanendra SinghSickle cell disease (SCD) poses a foremost global health challenge, especially in developing countries where misdiagnosis is common. Timely diagnosis and treatment are crucial in preventing its high mortality rates. To enhance diagnosis, we've created an automated SCD screening system utilizing deep learning. Our method involves two networks that enhance low-resolution microscope images and accurately identify sickle cells. In blind tests, we achieved an impressive accuracy of 96.7%. This costeffective, precise microscope stands as a valuable tool for identifying SCD and addressing blood disorders in resource-constrained areas. © 2023 IEEE.