AI Approach for Enhanced Thalassemia Diagnosis Using Blood Smear Images...20th International Conference on Wearable Micro and Nano Technologies for Personalized Health - pHealth 2024, May 27-29, 2024, Rende, Italy.

This paper aims to propose an approach leveraging Artificial Intelligence (AI) to diagnose thalassemia through medical imaging. The idea is to employ a U-net neural network architecture for precise erythrocyte morphology detection and classification in thalassemia diagnosis. This accomplishment was...

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Publicado en:Studies in Health Technology & Informatics Vol. 314; pp. 123 - 125
Autores principales: MAZZUCA, Daniela, BERGANTIN, Fulvio, MACRÌ, Davide, ZINNO, Francesco, FORESTIERO, Agostino
Formato: proceedings research Journal Article
Publicado: Sage Publications Inc. 2024
Acceso en línea:Ver este registro en EBSCOhost
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        atl: AI Approach for Enhanced Thalassemia Diagnosis Using Blood Smear Images...20th International Conference on Wearable Micro and Nano Technologies for Personalized Health - pHealth 2024, May 27-29, 2024, Rende, Italy.
      aug:
        au:
          MAZZUCA, Daniela
          BERGANTIN, Fulvio
          MACRÌ, Davide
          ZINNO, Francesco
          FORESTIERO, Agostino
        affil: Immunohaematology Section, Annunziata Hospital, Via F. Migliori, CS, Italy.
      sug:
        subj:
          Thalassemia Diagnosis
          Artificial Intelligence Utilization
          Image Enhancement
          Image Processing, Computer Assisted
          Human
          Deep Learning
          Individualized Medicine
          Neural Networks (Computer)
          Erythrocytes Pathology
          Thalassemia Classification
          Semantics
          Research Methodology
          Precision
          Disease Management
          Congresses and Conferences Italy
          Italy
      ab: This paper aims to propose an approach leveraging Artificial Intelligence (AI) to diagnose thalassemia through medical imaging. The idea is to employ a U-net neural network architecture for precise erythrocyte morphology detection and classification in thalassemia diagnosis. This accomplishment was realized by developing and assessing a supervised semantic segmentation model of blood smear images, coupled with the deployment of various data engineering techniques. This methodology enables new applications in tailored medical interventions and contributes to the evolution of AI within precision healthcare, establishing new benchmarks in personalized treatment planning and disease management.
      pubtype: Academic Journal
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        research
        Journal Article
      ougenre: Article
    language: English
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