Advanced Deep Learning Techniques for Sickle Cell Anaemia Detection

Year : 2024 | Volume : | : | Page : –
By
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Aditya Bagdi,

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V.S. Baste,

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Dhaval Bang,

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Om Bhandare,

  1. Student, Department of Electronics and Telecommunication Engineering, Sinhgad College of Engineering (Smt. Kashibai Navale College of Engineering), Savitribai Phule Pune University,, Pune, India
  2. Professor, Department of Electronics and Telecommunication Engineering, Sinhgad College of Engineering (Smt. Kashibai Navale College of Engineering), Savitribai Phule Pune University,, Pune, India
  3. Student, Department of Electronics and Telecommunication Engineering, Sinhgad College of Engineering (Smt. Kashibai Navale College of Engineering), Savitribai Phule Pune University,, Pune, India
  4. Student, Department of Electronics and Telecommunication Engineering, Sinhgad College of Engineering (Smt. Kashibai Navale College of Engineering), Savitribai Phule Pune University,, Pune, India

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Sickle Cell Anemia (SCA) is a prevalent genetic blood disorder characterized by the presence of abnormal hemoglobin, resulting in the distinctive sickle shape of red blood cells. Timely and accurate identification of Sickle Cell Anemia (SCA) is essential for effective management and treatment. This study presents a new method that utilizes Convolutional Neural Networks (CNNs), a deep learning model particularly effective for image analysis. The process involves using microscopic images of blood smears from patients, which are pre-processed to improve contrast, adjust intensity, and reduce noise. CNN architectures are then employed to automatically identify complex features within the images. The trained CNN model learns to identify distinctive features directly from the images, allowing it to effectively discern subtle differences between normal red blood cells (RBCs) and those that are sickled. The advantages of using CNNs for SCA detection are manifold. Firstly, CNNs automate feature extraction, reducing the potential for human error and improving the robustness of the detection process. Secondly, they have demonstrated high accuracy in various image classification tasks, effectively differentiating between normal and sickled RBCs, even in challenging cases with overlapping cells or poor image quality. Thirdly, CNN models are scalable and can process large volumes of data quickly and efficiently, benefiting screening programs in regions with high SCA prevalence. Additionally, CNN models can continuously improve with additional training data, enhancing their accuracy and generalization capabilities over time. Finally, given advancements in mobile computing, CNN based detection systems can potentially be integrated with mobile devices, enabling point-of care diagnostics in remote and resource-limited settings.

Keywords: Sickle Cell Anaemia, Convolutional Neural Network, Deep Learning, Image Analysis, Blood Smear Images, Early Detection, Diagnostic tool.

How to cite this article:
Aditya Bagdi, V.S. Baste, Dhaval Bang, Om Bhandare. Advanced Deep Learning Techniques for Sickle Cell Anaemia Detection. Research & Reviews: A Journal of Medicine. 2024; ():-.
How to cite this URL:
Aditya Bagdi, V.S. Baste, Dhaval Bang, Om Bhandare. Advanced Deep Learning Techniques for Sickle Cell Anaemia Detection. Research & Reviews: A Journal of Medicine. 2024; ():-. Available from: https://journals.stmjournals.com/rrjom/article=2024/view=0

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Ahead of Print Subscription Review Article
Volume
Received 17/07/2024
Accepted 08/10/2024
Published 11/11/2024
99