Abstract
Globally, one among the most prominent cause of death is lung cancer, which is the most malignant tumorin human health. Hence, automatically detecting or diagnosing lung disease as of computerized tomographic scan image is the necessary application. Numerous cancer classification systems have been engendered for this. However, it is not easy to determine the presence of tumors in small nodules. Hence, a novel Aquila-optimized mish dropout-deep convolutional neural network (AmiD-DCNN) cancer classification system was proposed in this paper. The noises are eliminated by utilizing an adaptive median filter initially and the Chi-square distribution adapted contrast limited adaptive histogram equalization is utilized to elevate the contrast. The residual unity AlexNet is utilized to segment the lung regions from the preprocessed image; also the Jaccard similarity and quadratic kernel-induced profuse clustering are employed to extract the cancerous region. The features are extracted after those steps, which are then fed to the Amid-DCNN classifier to classify cancer. The experiments are evaluated along with analogized with the benchmark models. The proposed model’s efficient performance was demonstrated by the experimental outcomes.
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Reddy, T.P.K., Bharathi, P.S. Lung cancer detection using novel residual unity AlexNet-based optimized mish dropout-deep convolutional neural network. Soft Comput (2023). https://doi.org/10.1007/s00500-023-08970-8
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DOI: https://doi.org/10.1007/s00500-023-08970-8
Keywords
- Adaptive median filter (AMF)
- Chi-square distribution adapted contrast limited adaptive histogram equalization (Chi-CLAHE) algorithm
- Residual unity AlexNet (RU-AlexNet)
- Jaccard similarity and quadratic kernel-induced profuse clustering (JQPC)
- Aquila-optimized mish dropout-deep convolutional neural network (AmiD-DCNN)