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An advanced diagnostic ColoRectalCADx utilises CNN and unsupervised visual explanations to discover malignancies

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Abstract

Colorectal cancer (CRC) is one of the most lethal kinds of cancer, so early detection is critical. Three datasets, namely CNN transfer learning with discrete wavelet transform (DWT), discrete cosine transform (DCT), and support vector machines (SVMs), were used to find CRC. In these instances, a quick and precise visual diagnosis of polyps is needed in the current scenario. The proposed process involves four distinct phases. First and foremost, convolutional neural networks (CNNs) are developed to test the efficacy of the model. Further, a transfer learning approach was incorporated using SVM and LSTM. Using the K-means technique, a visual explanation is finally presented. This system works with the balanced Hyper Kvasir and mixed datasets, which are made up of CVC Clinic DB, Kvasir2, and Hyper Kvasir. The system is called "ColoRectalCADx". The convolutional neural network (CNN) models are ResNet-50V2, DenseNet-201, VGG-16, and RDV-22. The system achieved the highest accuracy with CNN DesnseNet-201 in Hyper Kvasir (98.92% training, 98.91% testing, 93.62% SVM training, and 95.87% SVM tests). CNN DenseNet-201 also achieved the highest accuracy with the mixed dataset (98.91% training, 96.13% testing, 95.41% SVM training, and 94.86% SVM testing). The process involved three phases, namely individual CNN, combination of CNN with SVM, and combination of CNN, LSTM, and SVM. After three phases of the system, across both datasets, the CNN + SVM + LSTM combination was proven to be the most effective. Finally, the unsupervised K-means learning algorithm extracts the location of any cancerous polyps and upon classification using SVM classifier resulted with an accuracy of 80%. The K-means algorithm, which uses segmented images as input, accurately predicts the sites of tumours in colorectal cancer patients.

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Data availability

The data were collected from publicly accessible colonoscopy datasets, namely CVC Clinic DB, Kvasir 2, and Hyper Kvasir for the project. Even though this work with internal human organs is available to the public via Internet sources, it has not been deemed ethical by official authorities. Data are publicly available from the following websites: CVC Clinic DB dataset was obtained from https://www.kaggle.com/datasets/balraj98/cvcclinicdb. Kvasir2 Dataset was obtained from https://datasets.simula.no/kvasir/. Hyper Kvasir dataset was obtained from https://datasets.simula.no/hyper-kvasir/. In this research paper, the combining of three datasets is presented as a new dataset known as a mixed dataset.

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Acknowledgements

The authors of this article are grateful to Chennai's SRM Institute of Science and Technology. Our work has been delegated to artificial intelligence, machine learning, and deep learning laboratories with advanced GPU systems. This initiative receives no funding from outside sources.

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Authors and Affiliations

  1. Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203, India

    Akella S. Narasimha Raju & Kayalvizhi Jayavel

  2. Department of Electronics and Communication Engineering, School of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203, India

    T. Rajalakshmi

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  1. Akella S. Narasimha Raju
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Correspondence to Akella S. Narasimha Raju.

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Raju, A.S.N., Jayavel, K. & Rajalakshmi, T. An advanced diagnostic ColoRectalCADx utilises CNN and unsupervised visual explanations to discover malignancies. Neural Comput & Applic 35, 20631–20662 (2023). https://doi.org/10.1007/s00521-023-08859-5

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