• G. Aasritha Mandara
• DR.M.Sailaja

Doctors require assistance with retrieving medical images in order to make accurate diagnosis, and reliability and speed are essential requirements. This work proposes a hybrid K-Nearest Neighbor (KNN) and Dyadic Gabor Wavelet Filter Bank (DGWFB) architecture to develop a power-efficient medical image retrieval system. DGWFB gathers texture and edge information at various resolutions, whereas Hybrid KNN increases classification accuracy using adaptive similarity metrics. The architecture is designed for VLSI implementation since portable medical equipment must have low power, scalability, and real-time performance. Results from simulations confirm that the solution achieves a decent balance between retrieval accuracy, speed, and hardware efficiency. The proposed method thus offers a reliable and energy-efficient solution for future applications involving the retrieval of medical images.

Hybrid K-Nearest Neighbor (KNN), Dyadic Gabor Wavelet Filter Bank (DGWFB), Medical Image Retrieval, VLSI Implementation, Power Efficiency, Real-Time Processing

"Hybrid KNN and Dyadic Gabor Wavelet Filter Bank for a Power-Efficient VLSI-Based Medical Image Retrieval", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 9, page no.d12-d26, September-2025, Available :http://www.jetir.org/papers/JETIR2509303.pdf

"Hybrid KNN and Dyadic Gabor Wavelet Filter Bank for a Power-Efficient VLSI-Based Medical Image Retrieval", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 9, page no. ppd12-d26, September-2025, Available at : http://www.jetir.org/papers/JETIR2509303.pdf