FPGA-Based Computer-Aided Diagnosis for Kidney CT Images Abnormality Classification Using NSCT and a Modified YOLOv11 (The International Journal for Scientific Research, 2026)
Kidney disease diagnosis often relies on expert radiologists and advanced imaging analysis, which may be limited or unavailable in remote healthcare settings. This paper presents an FPGA-oriented computer-aided diagnosis scheme for binary kidney abnormality classification (normal vs. abnormal) that combines kidney ROI extraction with nonsubsampled contourlet transform (NSCT) feature compaction and a modified YOLOv11 classifier. After normalization and kidney segmentation, NSCT is applied to the ROI and only the final low-frequency sub-band is retained as a compact, structure-preserving representation for classification. The key novelty is a lightweight YOLOv11 classification variant tailored for deployment constraints by reducing depth scaling and removing attention modules to support efficient fixed-point implementation while retaining discriminative power with NSCT low-frequency inputs. The proposed method has been achieved better results as shown in accuracy, sensitivity, and specificity (i.e.: 97.45%, 98.23%, and 97.45%) respectively. The higher performance of Zynq based hardware shows 68 ms latency in image, and 14.7 images/ throughput, performing better real-time for kidney CT disease classification.
Keywords: CT Images; Nonsubsampled Contourlet Transform; YOLOv-11