Abstract
The present study presents an alternative analytical workflow that
combines mid-infrared (MIR) microscopic imaging and deep learning to
diagnose human lymphoma and differentiate between small and large cell
lymphoma. We could show that using a deep learning approach to analyze
MIR hyperspectral data obtained from benign and malignant lymph node
pathology results in high accuracy for correct classification, learning
the distinct region of 3900 cm-1 to 850
cm-1. The accuracy is above 95% for every pair of
malignant lymphoid tissue and still above 90% for the distinction
between benign and malignant lymphoid tissue for binary classification.
These results demonstrate that a preliminary diagnosis and subtyping of
human lymphoma could be streamlined by applying a deep learning approach
to analyze MIR spectroscopic data.
Keywords: deep learning, mid-infrared microscopic imaging,
diffuse large B-cell-lymphoma, follicular lymphoma