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Histopathology-based diagnosis depends on specialized expertise and is complex, while limited expert availability can contribute to inefficient resource use and misdiagnosis. 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Feature Analysis Using DenseNet201 and SVM With Histopathological Images:  \nA Super-Resolution Approach  \nPragati Patharia1 | B. Surya Prasad Rao2 | Prabira Kumar Sethy3  | Ashoka Kumar Ratha3 | Aziz Nanthaamornphong4   \n1Department of ECE, Guru Ghasidas Vishwavidyalaya, Bilaspur, India | 2PVP Siddhartha Institute of Technology, Vijaywada, India | 3Department of  \nElectronics, Sambalpur University, Sambalpur, Odisha, India | 4College of Computing, Prince of Songkla University, Phuket, Thailand Correspondence: Prabira Kumar Sethy ([prabirsethy.05@gmail.com](prabirsethy.05@gmail.com)) | Aziz Nanthaamornphong ([aziz.n@phuket.psu.ac.th](aziz.n@phuket.psu.ac.th))  \nReceived: 29 August 2024 | Revised: 19 August 2025 | Accepted: 15 December 2025  \nKeywords: colon cancer | DenseNet201 | FSRCNN | histopathology images | lung cancer | medical imaging | SVM  \nABSTRACT  \nBackground and Aim: The worldwide healthcare system faces significant challenges due to the increasing prevalence of lung and colon cancer, highlighting the need for timely and accurate detection to improve patient prognosis. The precision of cancer diagnosis is highly dependent on the expertise of histopathologists, making it a complex and demanding endeavor. A shortage of sufficiently skilled experts can lead to the ineffective allocation of healthcare resources, potential misdiagnoses, and unwarranted interventions, ultimately threatening patient well-being. However, technological advancements have introduced deep learning as a powerful tool in clinical applications, particularly in the field of medical imaging. This study aims to develop a novel diagnostic method leveraging deep learning techniques to enhance the accuracy of lung and colon cancer detection.  \nMethods: The study utilized the LC25000 dataset, which comprises 25 000 histopathological images of lung and colon tissue. A novel approach was implemented using a Fast Super-Resolution Convolutional Neural Network (FSRCNN) for image enhancement and a Support Vector Machine (SVM) model based on DenseNet201 for classification. The FSRCNN was employed to improve the clarity and detail of images by increasing their resolution, which is crucial for accurate cancer detection.  \nResults: The proposed model demonstrated superior performance compared to existing Convolutional Neural Network (CNN) models. It achieved an overall accuracy of 98.00%, precision of 98.10%, sensitivity of 98%, F1 score of 0.98, and specificity of 99.50% . These metrics indicate a significant enhancement in diagnostic accuracy and reliability, underscoring the effectiveness of the FSRCNN and SVM-based DenseNet201 model.  \nConclusion: The implementation of the FSRCNN and SVM-based DenseNet201 model provided substantial improvements in the detection of lung and colon cancer, as evidenced by high accuracy and specificity metrics. While the LC25000 dataset offered a solid foundation for this analysis, future research should aim to validate the model's effectiveness using a broader array of diverse and extensive datasets. Additionally, integrating supplementary diagnostic techniques, such as genetic data and electronic health records, could further enhance the model's diagnostic precision and practical application in cancer diagnosis.  \nThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \n© 2025 The Author(s). Cancer Reports published by Wiley Periodicals LLC.  \nCancer Reports, 2025; 8:e70439 1 of 15  \n[https://doi.org/10.1002/cnr2.70439](https://doi.org/10.1002/cnr2.70439)  \n1 | Introduction  \nThe global burden of cancer-related illness and mortality is significantly influenced by lung and colon cancer. On an annual basis, lung cancer results in approximately 1.76 million fatalities and ","cbCaiuas4pXZf1P8","https://ap.wps.com/l/cbCaiuas4pXZf1P8","pdf",1570380,15,"English","# Abstract\n## Background and Aim\n## Methods\n## Results\n## Conclusion\n# Introduction","[{\"question\":\"What problem does this study address in cancer diagnosis?\",\"answer\":\"The study targets the need for timely, accurate detection of lung and colon cancer severity from histopathological images, which is challenging when expert availability is limited and manual evaluation can be slow and subjective.\"},{\"question\":\"Which deep-learning components are used for the proposed method?\",\"answer\":\"The method uses FSRCNN for fast super-resolution image enhancement and a DenseNet201-based SVM classification model to improve diagnostic performance.\"},{\"question\":\"What performance results are reported for the proposed model?\",\"answer\":\"The model reports an overall accuracy of 98.00%, precision of 98.10%, sensitivity of 98%, F1 score of 0.98, and specificity of 99.50%, indicating improved diagnostic accuracy and reliability.\"}]","Enhanced Diagnosis of Lung and Colon Cancer Severity Through Deep Feature Analysis Using DenseNet201 and SVM With Histopathological Images - A Super-Resolution Approach | PDF",1790180107,38]