Detection of pneumonia and tuberculosis disease in the bovine lung by machine learning techniques using histopathological data

Bovine tuberculosis (bTB) is a zoonotic, infectious, and chronic disease associated with significant economic losses, persisting as an endemic concern in various regions globally. Histopathological examinations are a crucial diagnostic method, particularly with the identification of Langhans giant cells, which act as a distinctive marker. The study focuses on the differential diagnosis of tuberculosis from other types of pneumonia utilizing transfer learning. Additionally, the work aims to enhance bTB diagnosis by employing the YOLOv8 algorithm to detect Langhans giant cells in histopathological images. While machine learning algorithms such as Logistic Regression, Support Vector Machines, and Random Forest algorithms reveal a notable accuracy of 98.6% in classification task, YOLOv8 demonstrates high precision in localizing Langhans giant cells. These findings make a valuable contribution to the advancement of bTB diagnostic capabilities, particularly in distinguishing tuberculosis from other pulmonary conditions.