WEB system for classification of Visceral Leishmaniasis with automatic microscopic image collection

• Lucas B. M. de Souza UFPI
• Armando L. Borges UFPI
• Viviane B. L. Dias UFPI
• Clésio de A. Gonçalves UFPI / IFSertãoPE
• Daniel de A. Viana UECE
• Ana Carolina L. Pacheco UFPI
• Romuere R. V. e Silva UFPI

DOI: https://doi.org/10.5753/sbcas_estendido.2024.2817

Abstract

This paper proposes a web application and an automated platform for the diagnosis of visceral leishmaniasis through microscopic images. Employing a deep learning approach, specifically the U-Net model, the system analyzes slide images to detect the Leishmania parasite, improving the diagnosis and treatment of the disease. The differential lies in the ability to automatically capture images, reducing the need for manual manipulation and speeding up the diagnostic process. The results highlight the model’s accuracy of 85.1% and sensitivity of 72.2% in identifying the parasites, demonstrating the application’s potential in clinical practice.

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