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

Authors: Anirudh K. Mangore; Geeta S. Navale; Jawahar Sambhaji Gawade; Shailesh Pramod Bendale; Mubin Tamboli; Amol V. Dhumane

Addresses: Department of Computer Science and Engineering, D.Y. Patil Agriculture and Technical University Talsande, Kolhapur, Maharashtra, India ' Department of Computer Engineering, Sinhgad Institute of Technology and Science, Pune, Maharashtra, India ' Department of Information Technology, SVPM's College of Engineering Malegaon (BK), Baramati, Pune, Maharashtra – 413115, India ' Department of Computer Engineering, N.B.N. Sinhgad School of Engineering, Pune, Maharashtra, India ' Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune, Maharashtra, India ' Department of Computer Science and Engineering, Symbiosis Institute of Technology, Pune, Maharashtra, India

Abstract: 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%.

Keywords: dense parallel convolutional neural network; PraNet; grey level run length matrix; parallel convolutional neural network matrix; DenseNet.

DOI: 10.1504/IJBET.2025.147075

International Journal of Biomedical Engineering and Technology, 2025 Vol.48 No.2, pp.111 - 137

Received: 24 Jun 2024
Accepted: 02 Oct 2024
Published online: 10 Jul 2025 *

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