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A Novel Convolutional Neural Network Model Based on Beetle Antennae Search Optimization Algorithm for Computerized

Dechao Chen, Xiang Li, Shuai Li

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    This study introduces an optimized Convolutional Neural Network (CNN) using the Beetle Antennae Search (BAS) algorithm for faster and more accurate medical image diagnosis, specifically for intracranial hemorrhage detection.

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    Area of Science:

    • Artificial Intelligence
    • Medical Imaging
    • Computer Science

    Background:

    • Convolutional Neural Networks (CNNs) are vital for medical image diagnosis.
    • Traditional CNNs suffer from slow training and suboptimal accuracy due to parameter initialization issues.

    Purpose of the Study:

    • To enhance CNN performance in medical imaging diagnosis.
    • To address the limitations of slow training and low accuracy in traditional CNNs.

    Main Methods:

    • Proposed a novel CNN optimization method utilizing the Beetle Antennae Search (BAS) algorithm.
    • Optimized initial CNN parameters using the BAS algorithm.
    • Developed and applied a pretrained BAS-CNN model for intracranial hemorrhage diagnosis.

    Main Results:

    • The BAS-optimized CNN achieved superior diagnostic performance compared to traditional CNNs.
    • Achieved 93.9394% diagnostic accuracy and 100% recall for intracranial hemorrhage detection.
    • Demonstrated significantly faster diagnosis, processing 66 CT images in 0.1596 seconds.

    Conclusions:

    • The proposed BAS-optimized CNN model offers improved accuracy and speed for medical image analysis.
    • This method presents a promising advancement over traditional CNNs and other optimization algorithms for intracranial hemorrhage diagnosis.