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Lung and Colon Cancer Classification of Histopathology Images Using Convolutional Neural Network

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Abstract

The greatest significant contributor to cancer-related morbidity and mortality worldwide is malignant lung tumors. Lung cancer frequency has been seen to be on the rise recently. Lung cancer histopathology diagnosis is a crucial part of the patient’s treatment. The current study aims to demonstrate the efficiency of convolutional neural networks for the identification of squamous cell carcinoma and adenocarcinoma of the lung and colon by investigating the diagnosis of histopathology images. Five state-of-the-art pre-trained (ImageNet) convolutional neural network architectures, VGG-19, InceptionResNetV2, DenseNet201, EfficientNetB6, and MobileNetV2, are employed in this investigation to tri-categorize lung cancer images (normal, adenocarcinoma, and squamous cell carcinoma), together with colon cancer images (normal and adenocarcinoma). Regularization strategies have been applied to fine-tune the learning rate for improving accuracy. The LC25000 dataset has been used to validate the proposed method. EfficientNetB6, VGG19, InceptionResNetV2, DenseNet201, and MobileNetV2 accuracy on test data is reported to be 93.12, 98.00, 97.92, 99.12, and 99.32 percent respectively.

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Data availability

The authors have used the publicly accessible datasets [48] for the current research.

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Authors and Affiliations

  1. Machine Vision and Intelligence Lab, Department of Computer Science and Engineering, National Institute of Technology Jamshedpur, Jharkhand, India

    Onkar Singh & Koushlendra Kumar Singh

  2. Computing Science Engineering, Indian Institute of Information Technology Kottayam, Kerala, India

    Kanchan Lata Kashyap

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This article is part of the topical collection “Advances in Machine Vision and Augmented Intelligence” guest edited by Manish Kumar Bajpai, Ranjeet Kumar, Koushlendra Kumar Singh and George Giakos.

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Singh, O., Kashyap, K.L. & Singh, K.K. Lung and Colon Cancer Classification of Histopathology Images Using Convolutional Neural Network. SN COMPUT. SCI. 5, 223 (2024). https://doi.org/10.1007/s42979-023-02546-x

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