{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T17:55:06Z","timestamp":1789754106332,"version":"4.0.1"},"reference-count":61,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T00:00:00Z","timestamp":1760659200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100011915","name":"Taylor\u2019s University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100011915","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Colon cancer (CC) image classification plays a key role in the diagnostic process in clinical contexts, especially as computational medical solutions become the trend for future radiology and pathology practices. This study presents a novel lightweight Convolutional Neural Network (CNN) model designed with effective data cleaning strategy for the classification and visualization of histopathology images of various colon cancer tissues.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Addressing the critical need for efficient diagnostic tools in colon cancer detection, the proposed model leverages a non-pretrained architecture optimized for performance in resource-constrained environments. Utilizing the NCT-CRC-HE-100K and CRC-VAL-HE-7K datasets, this model employs a parametric Gaussian distribution-based data cleaning approach to enhance data quality by removing outliers.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>With a total of 4,414,217 parameters and a total size of 16.9 megabytes, the model achieves a test accuracy of 0.990 \u00b1 0.003 with 95% level of confidence, which demonstrates high precision, recall, specificity, and F1 scores across various tissue classes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Comparative analysis with benchmark studies underscores the model\u2019s effectiveness, while discussions on underfitting and overfitting provide insights into potential fine-tuning strategies. This research presents a robust, lightweight solution for colon cancer histopathology image classification, offering a foundation for future advancements in colon cancer diagnostics with result visualization.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2025.1659010","type":"journal-article","created":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T04:13:13Z","timestamp":1760674393000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["A lightweight CNN for colon cancer tissue classification and visualization"],"prefix":"10.3389","volume":"15","author":[{"given":"Jie","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiwei","family":"Goh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Noor Zaman","family":"Jhanjhi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,10,17]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"26","DOI":"10.69882\/adba.csai.2025075","article-title":"Artificial intelligence in mammography: A study of diagnostic accuracy and efficiency","volume":"1","author":"Alswilem","year":"2025","journal-title":"Comput Syst Artif Intell"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.69882\/adba.ai.2025073","article-title":"Deep learning for automated breast cancer detection in ultrasound: A comparative study of four cnn architectures","volume":"1","author":"\u00c7akmak","year":"2025","journal-title":"Artif Intell Appl Sci"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2025.e41835","article-title":"Context aware machine learning techniques for brain tumor classification and detection\u2013a review","volume":"11","author":"Amjad","year":"2025","journal-title":"Heliyon"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2025.03.184","article-title":"Lungse-net: Enhanced lung cancer diagnosis via lightweight cnn model using histopathological images","volume":"260","author":"Adil","year":"2025","journal-title":"Proc Comput Sci"},{"key":"B5","doi-asserted-by":"publisher","first-page":"24994","DOI":"10.1038\/s41598-025-08954-8","article-title":"Enhanced melanoma and non-melanoma skin cancer classification using a hybrid lstm-cnn model","volume":"15","author":"Abohashish","year":"2025","journal-title":"Sci Rep"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-021-01343-4","article-title":"Deep learning in histopathology: The path to the clinic","volume":"27","author":"van der Laak","year":"2021","journal-title":"Nat Med"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-022-00701-z","article-title":"Transfer learning approach and nucleus segmentation with medclnet colon cancer database","volume":"36","author":"Reis","year":"2023","journal-title":"J Digital Imaging"},{"key":"B8","doi-asserted-by":"publisher","first-page":"8823","DOI":"10.1038\/s41598-023-35431-x","article-title":"Ensemble-based multi-tissue classification approach of colorectal cancer histology images using a novel hybrid deep learning framework","volume":"13","author":"Fadafen","year":"2023","journal-title":"Sci Rep"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.14569\/IJACSA.2022.0130936","article-title":"Tissue and tumor epithelium classification using fine-tuned deep cnn models","volume":"13","author":"Anju","year":"2022","journal-title":"Int J Advanced Comput Sci Appl"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.14569\/IJACSA.2021.0120880","article-title":"Deep learning predictive model for colon cancer patient using cnn-based classification","volume":"12","author":"Tasnim","year":"2021","journal-title":"Int J Advanced Comput Sci Appl"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2022.853294","article-title":"Early-stage alzheimer\u2019s disease prediction using machine learning models","volume":"10","author":"Kavitha","year":"2022","journal-title":"Front Public Health"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.3906\/elk-1801-259","article-title":"Classification of the likelihood of colon cancer with machine learning techniques using ftir signals obtained from plasma","volume":"27","author":"Toraman","year":"2019","journal-title":"Turkish J Electrical Eng Comput Sci"},{"key":"B13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10549-024-07361-3","article-title":"The contribution of second primary cancers to the mortality of patients with a first primary breast cancer","volume":"207","author":"Gon\u00e7alves","year":"2024","journal-title":"Breast Cancer Res Treat"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-022-03713-3","article-title":"Breast cancer detection in mammogram: Combining modified cnn and texture feature based approach","volume":"14","author":"Melekoodappattu","year":"2023","journal-title":"J Ambient Intell Humanized Computing"},{"key":"B15","first-page":"1","article-title":"Cnn-based method for lung cancer detection in whole slide histopathology images","volume-title":"Proc. 2019 4th Int. Conf. Smart Sustain. Technol. (SpliTech)","author":"\u0160ari\u0107","year":"2019"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.1016\/S1470-2045(22)00224-8","article-title":"Adebrelimab or placebo plus carboplatin and etoposide as first-line treatment for extensive-stage small-cell lung cancer (capstone-1): a multicentre, randomised, double-blind, placebo-controlled, phase 3 trial","volume":"23","author":"Wang","year":"2022","journal-title":"Lancet Oncol"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.1016\/j.jocn.2019.05.019","article-title":"Automated brain histology classification using machine learning","volume":"66","author":"Ker","year":"2019","journal-title":"J Clin Neurosci"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.1016\/j.irbm.2021.06.003","article-title":"A hybrid cnn-svm threshold segmentation approach for tumor detection and classification of mri brain images","volume":"43","author":"Khairandish","year":"2022","journal-title":"Irbm"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3038764","article-title":"Identification of colon cancer using multi-scale feature fusion convolutional neural network based on shearlet transform","volume":"8","author":"Liang","year":"2020","journal-title":"IEEE Access"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.1101\/2020.08.15.20175760","article-title":"The histological diagnosis of colonic adenocarcinoma by applying partial self supervised learning","author":"Bukhari","year":"2020","journal-title":"MedRxiv"},{"key":"B21","doi-asserted-by":"publisher","first-page":"104730","DOI":"10.1016\/j.compbiomed.2021.104730","article-title":"Deep learning for colon cancer histopathological images analysis","volume":"136","author":"Hamida","year":"2021","journal-title":"Comput Biol Med"},{"key":"B22","doi-asserted-by":"publisher","first-page":"104202","DOI":"10.1016\/j.engappai.2021.104202","article-title":"Colorectal histology tumor detection using ensemble deep neural network","volume":"100","author":"Ghosh","year":"2021","journal-title":"Eng Appl Artif Intell"},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.1101\/2021.03.17.21253390","article-title":"Enhancing histopathological colorectal cancer image classification by using convolutional neural network","volume":"15","author":"Shawesh","year":"2021","journal-title":"MedRxiv"},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.3390\/electronics10141662","article-title":"Deep learning techniques for the classification of colorectal cancer tissue","volume":"10","author":"Tsai","year":"2021","journal-title":"Electronics"},{"key":"B25","doi-asserted-by":"publisher","first-page":"8450","DOI":"10.3390\/app12178450","article-title":"An efficient deep learning approach for colon cancer detection","volume":"12","author":"Sakr","year":"2022","journal-title":"Appl Sci"},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/5269913","article-title":"Automated detection and characterization of colon cancer with deep convolutional neural networks","volume":"2022","author":"Hasan","year":"2022","journal-title":"J Healthcare Eng"},{"key":"B27","doi-asserted-by":"publisher","first-page":"100911","DOI":"10.1016\/j.neo.2023.100911","article-title":"Lung cancer lesion detection in histopathology images using graph-based sparse pca network","volume":"42","author":"Ram","year":"2023","journal-title":"Neoplasia"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2023.1240645","article-title":"An improved multi-scale gradient generative adversarial network for enhancing classification of colorectal cancer histological images","volume":"13","author":"Jiang","year":"2023","journal-title":"Front Oncol"},{"key":"B29","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.104172","article-title":"Crccn-net: Automated framework for classification of colorectal tissue using histopathological images","volume":"79","author":"Kumar","year":"2023","journal-title":"Biomed Signal Process Control"},{"key":"B30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/IJSKD.326629","article-title":"Automated system for colon cancer detection and segmentation based on deep learning techniques","volume":"15","author":"Azar","year":"2023","journal-title":"Int J Sociotechnology Knowledge Dev (IJSKD)"},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2024.e24403","article-title":"Yolo and residual network for colorectal cancer cell detection and counting","volume":"10","author":"Haq","year":"2024","journal-title":"Heliyon"},{"key":"B32","first-page":"1","article-title":"Resnet110 and mask recurrent convolutional neural network based detection and classification of colorectal cancer","volume-title":"2024 International Conference on Distributed Computing and Optimization Techniques (ICDCOT)","author":"Alzubaidi","year":"2024"},{"key":"B33","first-page":"113625","volume-title":"Hybrid deep learning model for automated colorectal cancer detection using local and global feature extraction","author":"Pacal","year":"2025"},{"key":"B34","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2025.3589390","article-title":"Hybrid deep learning model combining xception and resnet with backpropagation and sgd for robust lung and colon cancer classification","volume":"13","author":"Venkatachalam","year":"2025","journal-title":"IEEE Access"},{"key":"B35","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2025.3588407","article-title":"Multi-modal deep learning for lung cancer detection using attention-based inception-resnet","volume":"13","author":"Hosny","year":"2025","journal-title":"IEEE Access"},{"key":"B36","doi-asserted-by":"publisher","first-page":"6914","DOI":"10.1038\/s41598-024-56820-w","article-title":"Color-cadx: a deep learning approach for colorectal cancer classification through triple convolutional neural networks and discrete cosine transform","volume":"14","author":"Sharkas","year":"2024","journal-title":"Sci Rep"},{"key":"B37","doi-asserted-by":"publisher","first-page":"405","DOI":"10.4048\/jbc.2023.26.e45","article-title":"Artificial intelligence in breast cancer diagnosis and personalized medicine","volume":"26","author":"Ahn","year":"2023","journal-title":"J Breast Cancer"},{"key":"B38","doi-asserted-by":"publisher","first-page":"312","DOI":"10.3390\/diagnostics13020312","article-title":"A novel lightweight cnn architecture for the diagnosis of brain tumors using mr images","volume":"13","author":"Reddy","year":"2023","journal-title":"Diagnostics"},{"key":"B39","doi-asserted-by":"publisher","DOI":"10.1007\/s11760-022-02328-7","article-title":"Lightweight cnn model: Automated vehicle detection in aerial images","volume":"17","author":"Momin","year":"2023","journal-title":"Signal Image Video Process"},{"key":"B40","doi-asserted-by":"crossref","DOI":"10.1109\/GLOBECOM54140.2023.10437676","article-title":"Pre-trained lightweight deep learning models for surgical instrument detection: Performance evaluation for edge inference","author":"Ahmed","year":"2023"},{"key":"B41","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2023.3331278","article-title":"Ldmres-net: A lightweight neural network for efficient medical image segmentation on iot and edge devices","volume":"28","author":"Iqbal","year":"2023","journal-title":"IEEE J Biomed Health Inf"},{"key":"B42","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1097\/JCMA.0000000000001188","article-title":"Comparison of machine learning algorithms for automatic prediction of alzheimer disease","volume":"88","author":"Aslan","year":"2025","journal-title":"J Chin Med Assoc"},{"key":"B43","doi-asserted-by":"crossref","DOI":"10.1109\/CISES58720.2023.10183491","article-title":"Adam optimizer and categorical crossentropy loss function-based cnn method for diagnosing colorectal cancer","volume-title":"2023 international conference on computational intelligence and sustainable engineering solutions (CISES)","author":"Ghosh","year":"2023"},{"key":"B44","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3150924","article-title":"Malignancy detection in lung and colon histopathology images using transfer learning with class selective image processing","volume":"10","author":"Mehmood","year":"2022","journal-title":"IEEE Access"},{"key":"B45","first-page":"052068","article-title":"Na\u00efve bayes classifier models for predicting the colon cancer","volume-title":"IOP Conference Series: Materials Science and Engineering","author":"Salmi","year":"2019"},{"key":"B46","doi-asserted-by":"publisher","first-page":"104730","DOI":"10.1016\/j.compbiomed.2021.104730","article-title":"Deep learning for colon cancer histopathological images analysis","volume":"136","author":"Ben","year":"2021","journal-title":"Comput Biol Med"},{"key":"B47","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1007\/s42452-024-06127-2","article-title":"Colorectal cancer image recognition algorithm based on improved transformer","volume":"6","author":"Qin","year":"2024","journal-title":"Discover Appl Sci"},{"key":"B48","doi-asserted-by":"publisher","first-page":"106579","DOI":"10.1016\/j.bspc.2024.106579","article-title":"Transnetv: An optimized hybrid model for enhanced colorectal cancer image classification","volume":"96","author":"Tanveer","year":"2024","journal-title":"Biomed Signal Process. Control"},{"key":"B49","doi-asserted-by":"publisher","DOI":"10.3389\/frai.2025.1571075","article-title":"Detecting early gastrointestinal polyps in histology and endoscopy images using deep learning","volume":"8","author":"Intissar","year":"2025","journal-title":"Front Artif Intell"},{"key":"B50","doi-asserted-by":"publisher","first-page":"e70161","DOI":"10.1002\/ima.70161","article-title":"Supervised constructive learning-based model for identifying colorectal cancer tissue types from histopathological images","volume":"35","author":"Firildak","year":"2025","journal-title":"Int J Imaging Syst Technol"},{"key":"B51","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-022-12129-2","article-title":"Spatial neighborhood intensity constraint (snic) and knowledge-based clustering framework for tumor region segmentation in breast histopathology images","volume":"81","author":"Tan","year":"2022","journal-title":"Multimedia Tools Appl"},{"key":"B52","doi-asserted-by":"publisher","first-page":"651","DOI":"10.3390\/electronics12030651","article-title":"Segmentation of nucleus and cytoplasm from h&e-stained follicular lymphoma","volume":"12","author":"Saxena","year":"2023","journal-title":"Electronics"},{"key":"B53","doi-asserted-by":"publisher","first-page":"110141","DOI":"10.1016\/j.dib.2024.110141","article-title":"Caishi: A benchmark histopathological h&e image dataset for cervical adenocarcinoma in situ identification, retrieval and few-shot learning evaluation","volume":"53","author":"Yang","year":"2024","journal-title":"Data Brief"},{"key":"B54","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-022-00759-9","article-title":"Automated adrenal gland disease classes using patch-based center symmetric local binary pattern technique with ct images","volume":"36","author":"Sut","year":"2023","journal-title":"J Digital Imaging"},{"key":"B55","doi-asserted-by":"publisher","DOI":"10.32604\/cmc.2021.014599","article-title":"Epithelial layer estimation using curvatures and textural features for dysplastic tissue detection","volume":"67","author":"Adam","year":"2021","journal-title":"Computers Materials Continua"},{"key":"B56","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2311.10118","article-title":"Now and future of artificial intelligence-based signet ring cell diagnosis: A survey","volume":"296","author":"Meng","year":"2023","journal-title":"arXiv preprint arXiv:2311.10118"},{"key":"B57","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-023-10588-z","article-title":"A systematic review of deep learning-based cervical cytology screening: from cell identification to whole slide image analysis","volume":"56","author":"Jiang","year":"2023","journal-title":"Artif Intell Rev"},{"key":"B58","doi-asserted-by":"publisher","first-page":"2177","DOI":"10.3390\/cancers17132177","article-title":"Spp-segnet and se-densenet201: A dual-model approach for cervical cell segmentation and classification","volume":"17","author":"Wubineh","year":"2025","journal-title":"Cancers"},{"key":"B59","doi-asserted-by":"publisher","first-page":"12","DOI":"10.3390\/make7010012","article-title":"Advancing ai interpretability in medical imaging: a comparative analysis of pixel-level interpretability and grad-cam models","volume":"7","author":"Ennab","year":"2025","journal-title":"Mach Learn Knowledge Extraction"},{"key":"B60","doi-asserted-by":"publisher","first-page":"100447","DOI":"10.1016\/j.modpat.2024.100447","article-title":"Histoem: a pathologist-guided and explainable workflow using histogram embedding for gland classification","volume":"37","author":"Ferrero","year":"2024","journal-title":"Modern Pathol"},{"key":"B61","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2022.e08762","article-title":"Spatial analysis of histology in 3d: quantification and visualization of organ and tumor level tissue environment","volume":"8","author":"Ruusuvuori","year":"2022","journal-title":"Heliyon"}],"container-title":["Frontiers in Oncology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2025.1659010\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T04:13:16Z","timestamp":1760674396000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2025.1659010\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,17]]},"references-count":61,"alternative-id":["10.3389\/fonc.2025.1659010"],"URL":"https:\/\/doi.org\/10.3389\/fonc.2025.1659010","relation":{},"ISSN":["2234-943X"],"issn-type":[{"value":"2234-943X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,17]]},"article-number":"1659010"}}