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<p>Lung cancer poses a serious health risk, making early diagnosis essential for better survival outcomes. Detection of lung cancer involves a series of medical evaluations and imaging techniques to identify cancerous cells in the lungs. Computed Tomography (CT) images are most frequently used to recognize lung cancer since it has high resolution, enhanced clarity, and minimal noise and distortions. However, accurate detection of lung cancer is complex owing to variations in nodule size, shape, and boundary definition. Therefore, an innovative model named Wide Slice Residual Kronecker Network (WISeRKNet) has been developed to diagnose lung cancer from CT images. Initially, image pre-processing is applied by using homomorphic filtering. Subsequently, the extraction of nodules in the lung is performed by the Link-Net model. Subsequently, augmentation of the image is conducted, and then the process of feature extraction is applied to refine shape-based features. At last, diagnosing lung cancer is executed by the WISeRKNet and which combines the Wide Slice Residual Network (WISeR) and the Deep Kronecker Network (DKN). Moreover, the developed WISeRKNet model demonstrated superior performance, by achieving improved value in accuracy as 91.686%, True Positive Rate (TPR) as 90.485%, True Negative Rate (TNR) as 92.727%, Precision as 90.980% and F1 score as 90.484% on the Lung Cancer Computed Tomography Images database using 90% of the data for training.</p>

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WISeRKNet: wide slice residual Kronecker network for lung cancer detection based on CT images

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月19日

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  • Amgothu Shanthi,
  • S. Satheesh Kumar,
  • Srinivas Koppu

摘要

Lung cancer poses a serious health risk, making early diagnosis essential for better survival outcomes. Detection of lung cancer involves a series of medical evaluations and imaging techniques to identify cancerous cells in the lungs. Computed Tomography (CT) images are most frequently used to recognize lung cancer since it has high resolution, enhanced clarity, and minimal noise and distortions. However, accurate detection of lung cancer is complex owing to variations in nodule size, shape, and boundary definition. Therefore, an innovative model named Wide Slice Residual Kronecker Network (WISeRKNet) has been developed to diagnose lung cancer from CT images. Initially, image pre-processing is applied by using homomorphic filtering. Subsequently, the extraction of nodules in the lung is performed by the Link-Net model. Subsequently, augmentation of the image is conducted, and then the process of feature extraction is applied to refine shape-based features. At last, diagnosing lung cancer is executed by the WISeRKNet and which combines the Wide Slice Residual Network (WISeR) and the Deep Kronecker Network (DKN). Moreover, the developed WISeRKNet model demonstrated superior performance, by achieving improved value in accuracy as 91.686%, True Positive Rate (TPR) as 90.485%, True Negative Rate (TNR) as 92.727%, Precision as 90.980% and F1 score as 90.484% on the Lung Cancer Computed Tomography Images database using 90% of the data for training.