02627nas a2200313   4500000000100000008004100001260003200042653001300074653001900087653001800106653001200124653002100136653000800157653001500165653002400180100001500204700001200219700001100231700001800242700001400260700001000274700001300284245013600297856006300433300001200496520176200508022002502270020001802295       2024                            d  bSpringer Nature Switzerland10aMycetoma10aHistopathology10aDeep learning10aSenegal10aMycetoma species10aCNN10aBlack skin10aStain normalization1 aZinsou KMS1 aDiop CT1 aDiop I1 aTsirikoglou A1 aSiddig EE1 aSow D1 aNdiaye M00aTowards Rapid Mycetoma Species Diagnosis: A Deep Learning Approach for Stain-Invariant Classification on H&E Images from Senegal  uhttps://papers.miccai.org/miccai-2024/paper/1516_paper.pdf  a757-7673 a<p>Mycetoma, categorized as a Neglected Tropical Disease (NTD), poses significant health, social, and economic challenges due to its causative agents, which include both bacterial and fungal pathogens. Accurate identification of the mycetoma type and species is crucial for initiating appropriate medical interventions, as treatment strategies vary widely. Although several diagnostic tools have been developed over time, histopathology remains a most used method due to its quickness, cost-effectiveness and simplicity. However, its reliance on expert pathologists to perform the diagnostic procedure and accurately interpret the result, particularly in resource-limited settings. Additionally, pathologists face the challenge of stain variability during the histopathological analyses on slides. In response to this need, this study pioneers an automated approach to mycetoma species identification using histopathological images from black skin patients in Senegal. Integrating various stain normalization techniques such as macenko, vahadane, and Reinhard to mitigate color variations, we combine these methods with the MONAI framework alongside DenseNet121 architecture. Our system achieves an average accuracy of 99.34%, 94.06%, 94.45% respectively on Macenko, Reinhard and Vahadane datasets. The system is trained using an original dataset comprising histopathological images stained with Hematoxylin and Eosin (H&amp;E), meticulously collected, annotated, and labeled from various hospitals across Senegal. This study represents a significant advancement in the field of mycetoma diagnosis, offering a reliable and efficient solution that can facilitate timely and accurate species identification, particularly in endemic regions like Senegal.</p>
  a0302-9743, 1611-3349  a9783031723834