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Skip and chain connected deep fusion network for lung cancer screening

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Abstract

The second most prevalent and deadly disease in the world is lung cancer. It is become more difficult to detect lung cancer in its early stages in humans. A novel deep learning architecture is suggested in this study to identify lung cancer in its early stages. To distinguish between the diseased and healthy samples, the suggested deep learning structure facilitates in-depth analysis and the creation of superior feature maps. Two independent deep neural network models are combined in the proposed H-DNN (Hybrid deep neural networks) architecture. DNN-1 or SC-SDNN, the first neural network, is used to analyse spatial data, and DNN-2 or CC-FDNN, the second neural network, is used to evaluate frequency data. In order to prevent vanishing gradients, we also added the short connection in the first deep network and the chain connection in the second. Finally, to achieve a better result, we combine both neural networks. The first neural network uses spatial information from the original raw image as its training input, whereas the second neural network uses frequencies generated by wavelets. Our technique outperformed more traditional CNN and SVM classifiers, with a classification accuracy of 98.2%.

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  1. Manonmanium Sundaranar University, Tirunelveli, India

    T. Arumuga Maria Devi & V. I. Mebin Jose

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  1. T. Arumuga Maria Devi
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  2. V. I. Mebin Jose
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Correspondence to T. Arumuga Maria Devi.

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Devi, T.A.M., Jose, V.I.M. Skip and chain connected deep fusion network for lung cancer screening. Multimed Tools Appl 83, 39503–39522 (2024). https://doi.org/10.1007/s11042-023-17110-1

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