Dense parallel convolutional neural network for lung and colon cancer detection using histopathological images

Lung and colon cancer are deadly diseases, which cause mortality and morbidity. Here, the dense parallel convolutional neural network (DPCNN) is proposed for detecting lung and colon cancer by histopathological images (HI). Initially, the HI of the lung and colon are taken from the lung and colon cancer histopathological images dataset and subjected to pre-processing. A Medav filter is used to pre-process the image. The parallel reverse attention network (PraNet) is then used to segregate the cancer region. Next, feature extraction is carried out, and features like grey level run length matrix (GRLM) with local neighbourhood difference pattern (LNDP) are extracted. After this, lung and colon cancer detection is performed by the DPCNN approach. The DPCNN is a combination of DenseNet and parallel convolutional neural network (PCNN). The DPCNN obtained the true negative rate (TNR), accuracy, and true positive rate (TPR) of 92.044%, 92.773% and 93.099%.