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Wireless Distributive Personal Communication for Early Detection of Collateral Cancer Using Optimized Machine Learning Methodology

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Abstract

Computer-aided diagnosis plays a vital role in many medical analysis especially with CT Images. The diagnosis of colorectal cancer detection becomes more challenging since the perfect detection of polyps (An opacified fluid lie on the colon walls) is difficult even for advanced segmentation methods. Many methods have been already developed, which lacks in segmentation of structure. In-order to achieve an accurate segmentation, a machine learning based algorithm such as regression neural network enhanced with the augmented Lagrangian genetic algorithm (RNN-ALGA). Since the segmentation was processed for multispectral image, the optimization strategy was included in-order to process all the 2D slices with reduced time span. The segmentation was done in-order to process the differentiation of colons, partial volume effect and bowels. By using this algorithm (RNN-ALGA) it is possible to achieve 97 % accuracy with some minor error. The comparison results shows that this algorithms best suitable abdominal slices of CT image is due to its enhanced nonlinearity nature. This methodology was developed especially for the hospital applications. RNN-ALGA works virtually in the common cloud server with an effective wireless distributive system accessed by many clients placed at the remote regions (medical centers, hospitals etc.).

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Authors and Affiliations

  1. Department of Computer Science and Engineering, SVS College of Engineering, Coimbatore, Tamilnadu, India

    D. Sivaganesan

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Sivaganesan, D. Wireless Distributive Personal Communication for Early Detection of Collateral Cancer Using Optimized Machine Learning Methodology. Wireless Pers Commun 94, 2291–2302 (2017). https://doi.org/10.1007/s11277-016-3411-9

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  • DOI: https://doi.org/10.1007/s11277-016-3411-9

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