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Alzheimer's detection by Artificial Bee Colony and Convolutional Neural Network at Mobile Environment

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Abstract

Alzheimer's disease (AD) presents a significant challenge in healthcare, particularly in its early detection. In this paper, we will introduce an innovative methodology that leverages the synergies of the Artificial Bee Colony (ABC) algorithm and Convolutional Neural Network (CNN) within a mobile environment to enhance the detection and diagnosis of Alzheimer's. The proposed system architecture integrates the ABC algorithm for feature optimization and CNN for image classification, specifically designed for mobile platforms. Our methodology emphasizes the efficient and accurate analysis of brain scans, specifically tailored to tackle the computational constraints inherent in mobile devices. These findings indicate that the integration of ABC and CNN within a mobile context could serve as a viable solution for early and accessible detection of Alzheimer's, potentially facilitating timely intervention and improving patient outcomes.

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Funding

This work was supported by Major project of Natural Science Foundation of Education Department in Jiangsu Province (22KJA510008), Science and Technology Planning Project of Yangzhou City (YZ2022209), Jiangsu Province vocational education wisdom scene application "double teacher" master teacher studio (2021).

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  1. School of Electronic and Information Engineering/Carbon Based Low Dimensional Semiconductor Materials and Device Engineering Research Center of Jiangsu Province, Yangzhou Polytechnic Institute, Yangzhou, 225127, China

    Dan Shan, Fanfeng Shi & Tianzhi Le

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  1. Dan Shan
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  2. Fanfeng Shi
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Dan Shan wrote the main manuscript text, Fanfeng Shi and Tianzhi Le collected datas and prepared figures. All authors reviewed the manuscript.

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Correspondence to Dan Shan.

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Shan, D., Shi, F. & Le, T. Alzheimer's detection by Artificial Bee Colony and Convolutional Neural Network at Mobile Environment. Mobile Netw Appl (2024). https://doi.org/10.1007/s11036-024-02313-z

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