A Hybrid 2D Gaussian Filter and Deep Learning Approach with Visualization of Class Activation for Automatic Lung and Colon Cancer Diagnosis

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dc.authorid0000-0002-1897-9830
dc.authorid0000-0003-0086-0206
dc.authorid0000-0002-2306-6008
dc.authorid0000-0002-7981-2305
dc.authorid0000-0002-0060-1880
dc.contributor.authorTurk, Omer
dc.contributor.authorAcar, Emrullah
dc.contributor.authorIrmak, Emrah
dc.contributor.authorYilmaz, Musa
dc.contributor.authorBakis, Enes
dc.date.accessioned2026-01-24T12:29:09Z
dc.date.available2026-01-24T12:29:09Z
dc.date.issued2024
dc.departmentAlanya Alaaddin Keykubat Üniversitesi
dc.description.abstractCancer is a significant public health issue due to its high prevalence and lethality, particularly lung and colon cancers, which account for over a quarter of all cancer cases. This study aims to enhance the detection rate of lung and colon cancer by designing an automated diagnosis system. The system focuses on early detection through image pre-processing with a 2D Gaussian filter, while maintaining simplicity to minimize computational requirements and runtime. The study employs three Convolutional Neural Network (CNN) models-MobileNet, VGG16, and ResNet50-to diagnose five types of cancer: Colon Adenocarcinoma, Benign Colonic Tissue, Lung Adenocarcinoma, Benign Lung Tissue, and Lung Squamous Cell Carcinoma. A large dataset comprising 25 000 histopathological images is utilized. Additionally, the research addresses the need for safety levels in the model by using Class Activation Mapping (CAM) for explanatory purposes. Experimental results indicate that the proposed system achieves a high diagnostic accuracy of 99.38% for lung and colon cancers. This high performance underscores the effectiveness of the automated system in detecting these types of cancer. The findings from this study support the potential for early diagnosis of lung and colon cancers, which can facilitate timely therapeutic interventions and improve patient outcomes.
dc.identifier.doi10.1177/15330338241301297
dc.identifier.issn1533-0346
dc.identifier.issn1533-0338
dc.identifier.pmid39632623
dc.identifier.scopus2-s2.0-85211634149
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1177/15330338241301297
dc.identifier.urihttps://hdl.handle.net/20.500.12868/5143
dc.identifier.volume23
dc.identifier.wosWOS:001370086200001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSage Publications Inc
dc.relation.ispartofTechnology in Cancer Research & Treatment
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20260121
dc.subjectlung and colon cancer
dc.subjectGaussian (Blur) filter
dc.subjectResNet50
dc.subjectdeep learning
dc.titleA Hybrid 2D Gaussian Filter and Deep Learning Approach with Visualization of Class Activation for Automatic Lung and Colon Cancer Diagnosis
dc.typeArticle

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