earticle

영어

This Leishmaniasis is common skin lesion parasitic disease caused by Leishmania protozoan parasites on exposed body and its polymorphic nature complicates to diagnosis because the lesion may create confusion with other dermatoses likewise fungi, bacteria and non-infectious diseases. The molecular techniques, microscopy, culture, and rapid diagnostic test are conventional methods that are timeconsuming, expensive, susceptible to errors with limited resources in health care services. Early diagnosis with timely identification of multifaceted Leishmaniasis is aided to selection of therapy and provide comfort to patient to combating with it. The promising integration of artificial intelligence (AI) with medical diagnostics has efficacy in numerous fields of identification of diseases as in Dermatology research. The fast, efficient and automatic diagnosing of leishmaniasis with microscopic images of lesion's seamer with VGG-16 deep learning (DL) model is the approach to reach the objective of this designed research to identify the negative and positive results. The exceptional performance of designed VGG-16 is achieved with accuracy of 88.14%, precision 100%, sensitivity 77.42%, specificity 100%, F1-score 0.87%, and ROC curve 97%. The proposed modified VGG-16 model is more precise, swift, reliable, efficient, effectual, economical and user-friendly substitute to address all key factors than human resource to find the leishmaniasis affected that may support medical care services.