Deep learning neural network of adenocarcinoma detection in effusion cytology.

ABSTRACT:

Objective

Cytologic examination, which confirms the presence or absence of malignant cells, detects malignant cells from various organs, with adenocarcinoma as the most common histologic type. We developed a deep learning model to detect malignant cells in images obtained following effusion cytology.

Methods

The deep learning model was created using the YOLOv8 object detection algorithm (Roboflow, Inc) and 275 cases of adenocarcinoma comprising 12 182 images and 29 245 labels as well as 188 cases negative for malignancy comprising 1980 images.

Results

The adenocarcinoma test dataset exhibited Precision, Recall, F1, and mean average Precision scores of 0.909, 0.911, 0.910, and 0.955, respectively. The number of adenocarcinoma test images in which 1 or more malignant ce

SUBMITTER: Ikeda K 

PROVIDER: S-EPMC12628318 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature