Biomedical Image Analysis using Deep Learning for Disease Detection

K. Maneesha; N. Sruthi; K. Vanaja; N. Madhu Shankar; K. Niharika

Biomedical Image Analysis using Deep Learning for Disease Detection

Author(s)	K. Maneesha, N. Sruthi, K. Vanaja, N. Madhu Shankar, K. Niharika
Country	India
Abstract	Because biomedical images are non-invasive and may be obtained in real time, they are frequently utilized in medical diagnostics. Dense radio-frequency (RF) data sampling is necessary for conventional beamforming techniques, which raises the transmission and acquisition speeds of data. Although sparse sampling approaches can lower data requirements, they still require an effective algorithm for reconstructing images. At the moment, the Biomedical probe's raw radio-frequency (RF) channel data transfers slowly to the computer for image processing. This research investigates an efficient method of capturing fewer photos each sample to improve efficiency and reduce data usage. They employ a compressed sensing-inspired strategy, which is a mechanism for effective data gathering. With this innovative method, high-quality biomedical images will be produced more quickly and with less data usage. Biomedical wave propagation in tissues is simulated physically to generate echo responses from discrete sites. The dictionary of shift-variant bent waves created by these back-projection techniques is dependent on the particular sound excitation and acquisition procedures. Speckles, or tiny dots, in biomedical pictures can be segmented into distinct portions with the use of specific principles. The purpose of these rules is to use minimum amount of information. They swiftly and accurately reconstruct the images from imperfect data by utilizing a smart mathematical technique known as the Moore–Penrose pseudoinverse. The advantages of an optimized basis function design for high-quality B-mode image recovery from few RF channel data samples are shown by results on simulated and acquired phantoms.
Keywords	Biomedical images, Convolutional Neural Network (CNN), Deep Learning, MATLAB, Recurrent Neural Network, Radio-Frequency (RF).
Discipline	Engineering
Published In	Volume 4, Issue 2, March-April 2026
Published On	2026-04-04

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Advanced International Journal of Multidisciplinary Research

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Biomedical Image Analysis using Deep Learning for Disease Detection

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