Asial CNN: Assorted Scale Integrated Alternate Link Model Convolutional Neural Network for Lung Nodule Detection

Asial CNN: Assorted Scale Integrated Alternate Link Model Convolutional Neural Network for Lung Nodule Detection

© 2022 by IJETT Journal
Volume-70 Issue-11
Year of Publication : 2022
Authors : S. Parveen Banu, M. Syed Mohamed
DOI : 10.14445/22315381/IJETT-V70I11P237

How to Cite?

S. Parveen Banu, M. Syed Mohamed, "Asial CNN: Assorted Scale Integrated Alternate Link Model Convolutional Neural Network for Lung Nodule Detection," International Journal of Engineering Trends and Technology, vol. 70, no. 11, pp. 353-363, 2022. Crossref,

Early-stage lung cancer is characterized mostly by the presence of lung nodules, a common symptom of the illness. It is critical to have an imaging system that can identify lung nodules automatically and accurately. In addition to reducing the burden on radiologists, automatic detection minimizes the incidence of misdiagnosis. Despite their outstanding performance, convolutional neural networks (CNNs) need certain anchor parameters, such as the size, number, and aspect ratio of anchors, and have limited resilience when dealing with a wide range of lung nodule sizes. The ASIAL CNN (assorted scale integrated alternative link model convolutional neural network) is a solution to these issues by automatically predicting nodule location, radius, and offset without the need for any custom nodule/anchor parameters to be designed. Three-level parallelism in the SIAL CNN is achieved by varying the convolution kernel size for the inputs with multi-scale properties. Here, the precession layer's output is coupled to its succession stage input and the succession stage input of the following layer. Binary classifications like benign and malignant lung nodules may be processed using this method, as shown by the results it achieves. It was all done using a graphics processing unit (GPU). The LIDC-IDRI dataset indicated that our proposed ASIAL CNN architecture outperforms current approaches for lung nodule identification with an average accuracy of 92.45%.

Lung cancer, lung nodule, assorted scale integrated alternate link model convolutional neural network.

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