Machine aided diagnosis and melanoma: histopathological findings

The incidence of cutaneous melanoma has risen in recent years. Histopathological examination remains the gold standard for diagnosing cutaneous melanoma; however, it is often complex. The need to streamline workflows and develop new diagnostic support methods for cutaneous melanoma has driven increased research into the application of artificial intelligence. Our study is structured into three main sections: (i) automated silhouette definition and its diagnostic significance, (ii) extraction of nuclear features and classification modeling, and (iii) assessment of Breslow thickness. Automating clinical procedures provides invaluable support, leading to faster and more reliable sample evaluations. Our study can expedite the screening of whole slide image by prioritizing histopathology slides that exhibit high-risk melanoma features over those with low-risk nevus characteristics.