Paper ID:
IJSDIP_2025_VOLUME 13_ISSUE 4_156-163
Author's Name:
Dr. Venkata Reddy Adama, Dr. K Venugopal Rao, Dr. G. Koteswar Rao
Volume:
13
Issue:
Issue 4
Year:
2025
Pages:
Abstract
Brain tumor detection is an important medical image processing task because early and accurate identification of tumor regions can
support diagnosis, treatment planning, and clinical decision-making. Magnetic Resonance Imaging (MRI) is widely used for brain analysis due
to its high soft-tissue contrast and non-invasive nature. However, manual interpretation of MRI slices is time-consuming and may vary
depending on radiologist experience. Deep learning methods, especially Convolutional Neural Networks (CNNs), have shown strong
capability in automatic tumor classification and feature learning. Nevertheless, software-based CNN processing on general-purpose
processors may require high computation time, memory, and power, which limits its use in portable and real-time healthcare systems. This
paper proposes a low-power FPGA-based brain tumor detection framework using MRI image processing and deep learning. The proposed
system includes MRI image acquisition, preprocessing, tumor segmentation, feature extraction, lightweight CNN classification, and FPGA
based hardware acceleration. Preprocessing operations such as resizing, denoising, contrast enhancement, and normalization improve MRI
image quality. Segmentation identifies suspicious tumor regions, while the CNN classifier categorizes MRI images into normal or tumor
classes, and can be extended to glioma, meningioma, and pituitary tumor classes. Hardware optimization techniques such as fixed-point
arithmetic, quantization, pipelining, parallel multiply-accumulate units, memory reuse, and clock gating are used to reduce power
consumption and latency. Performance analysis demonstrates that the proposed FPGA-oriented system can provide high classification
accuracy, low processing delay, and improved energy efficiency compared with conventional CPU-based processing. The proposed
architecture is suitable for real-time embedded medical imaging, portable diagnostic devices, and edge-AI healthcare applications.
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