Design and Development of Deep Neuron Attention Network for Lung and Colon Cancer Detection
DOI:
https://doi.org/10.32628/CSEIT25112828Keywords:
Histopathological image, Lung cancer, Colon cancer, Deep Belief Neuron Attention NetworkAbstract
As Lung and colon cancers are most common types of malignant globally. Lung cancer is a harmful growth that starts in the lungs and colon cancer begins in the rectum and develops into malignant tumors, with adenocarcinoma being the most prevalent subtype. The diagnosis and treatment of LCC are dependent on histopathological techniques, which are often time-consuming and require skilled pathologists. The Deep Belief Neuron Attention Network (DBNA-Net) is introduced for effective LCC detection. The process starts by sourcing the images from the database, which is the input for preprocessing that is done by an un sharp filter. The tissue segmentation is done by the Pyramid Non-local U-Net (PN-UNet), and the Local Vector Pattern (LVP) with wavelet texture feature is extracted in the feature extract phase.
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