Advanced International Journal for Research

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Automated Erythrocyte Shape Investigation using Image Processing and Deep Learning

Author(s) Ms. VAISHNAVI S, Ms. VAISHALI MANJUNATH NAYAK, Ms. SHIVANI PATIL, Ms. WANI VAISHNAVI, Prof. Dr. KINNY GARG, Prof. Dr. SENBAGAVALLI G
Country India
Abstract Automated red blood cell (RBC) morphological
analysis is a critical component of modern hematological di
agnostics, enabling early identification of disorders such as
anemia, sickle cell disease, thalassemia, and hereditary shape
abnormalities. Manual microscopic examination suffers from
subjectivity, inconsistent accuracy, and limited accessibility in
low-resource healthcare environments. To address these chal
lenges, this paper presents a fully automated, low-cost RBC
classification system implemented on a Raspberry Pi using deep
learning and a comprehensive image-processing pipeline. A USB
digital microscope captures smear images, which undergo re
sizing, denoising, contrast enhancement, illumination correction,
segmentation, and normalization. A fine-tuned InceptionV3 CNN
classifies RBCs into nine morphological categories, including
sickle cells, elliptocytes, spherocytes, and target cells. A Streamlit
interface provides real-time visualization, classification distribu
tion plots, and automated PDF report generation. Trained on the
Pathologics RBC dataset , the proposed model achieved 98.89%
accuracy, precision of 1.0, recall of 1.0, and an F1-score of 1.0.
Experimental evaluation confirms that embedded devices such
as Raspberry Pi can effectively deploy deep-learning models for
point-of-care medical diagnostics.
Keywords RBC morphology analysis, image processing, deep learning, CNN, InceptionV3, segmentation, preprocessing pipeline, classification, TensorFlow Lite, Raspberry Pi, USB digital microscope,, real-time inference, hematological diagnostics
Field Engineering
Published In Volume 6, Issue 6, November-December 2025
Published On 2025-12-09
DOI https://doi.org/10.63363/aijfr.2025.v06i06.2454
Short DOI https://doi.org/hbf94s
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