Towards Rapid Mycetoma Species Diagnosis: A Deep Learning Approach for Stain-Invariant Classification on H&E Images from Senegal

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&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.