Rapid improvements in ultrasound imaging technology have made it much more useful for screening and diagnosing breast problems. Local-speckle-noise destruction in ultrasound breast images may impair image quality and impact observation and diagnosis. It is crucial to remove localized noise from images. In the article, we have used the hybrid deep learning technique to remove local speckle noise from breast ultrasound images. The contrast of ultrasound breast images was first improved using logarithmic and exponential transforms, and then guided filter algorithms were used to enhance the details of the glandular ultrasound breast images. In order to finish the pre-processing of ultrasound breast images and enhance image clarity, spatial high-pass filtering algorithms were used to remove the extreme sharpening. In order to remove local speckle noise without sacrificing the image edges, edge-sensitive terms were eventually added to the Logical-Pool Recurrent Neural Network (LPRNN). The mean square error and false recognition rate both fell below 1.1% at the hundredth training iteration, showing that the LPRNN had been properly trained. Ultrasound images that have had local speckle noise destroyed had signal-to-noise ratios (SNRs) greater than 65 dB, peak SNR ratios larger than 70 dB, edge preservation index values greater than the experimental threshold of 0.48, and quick destruction times. The time required to destroy local speckle noise is low, edge information is preserved, and image features are brought into sharp focus.

Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R and D Institute of Science and Technology Chennai 600062 India

Department of Electronics and Communications Engineering Annamacharya Institute of Technology and Sciences Rajampet 516126 India

Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur 603203 India

Department of Telecommunications Faculty of Electrical and Communication Engineering Brno University of Technology Technicka 12 616 00 Brno Czech Republic

Faculty of Management Comenius University Bratislava Odbojarov 10 820 05 Bratislava Slovakia

School of Computer Science and Engineering Vellore Institute of Technology Vellore 632014 India

School of Information Technology and Engineering Vellore Institute of Technology Vellore 632014 India

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