A Lightweight CNN Framework for Real Time Liver Lesion Classification from CT Images with YOLOv8 Guided Localization
TL;DR
The findings show that early liver cancer screening is a good fit for a lightweight, scalable classification pipeline, and future research should focus on improving small lesion localisation.
Abstract
In order to improve patient outcomes, rapid and repeatable characterisation of hepatic tumours by computed tomography (CT) is essential. Liver cancer is still one of the most deadly cancers in the world. Although traditional volumetric (3D) segmented networks accurately identify lesions, their utility for real time screenings on commodity hardware is restricted due to their high computational cost and delay. A compact a convolutional neural network (CNN) which classifies hepatic regions as benign, malignant, or normal is the main contribution of this works lightweight classification framework. An upstream, anchor less YOLOv8 stage provides candidate regions for lesion localisation. A semi supervised U-Net pipeline is used to transform volumetric research from the LiTS, 3DIRCADb, along with CHAOS collections into annotated 2D axial slices. While the upstream localisation step runs at about 30 ms per slice, the CNN classification stage achieves a mean per class accuracy of about 95% (0.96 normal, 0.94 benign, and 0.95 malignant) with minor, symmetric inter class confusion on the curated test set. A Flask web interface that overlays areas, confidence scores, as well as lesion labels in real time, provides access to the framework. The findings show that early liver cancer screening is a good fit for a lightweight, scalable classification pipeline, future research should focus on improving small lesion localisation.