[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123436-en":3,"doc-seo-123436-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123436,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Comparative Analysis of Machine Learning Models for Lung Cancer Detection Using CT Scan Images","Machine learning models support lung cancer detection by extracting image features, reducing diagnostic errors, and enabling earlier decision-making than manual interpretation. A dataset of 995 CT images was resized to 128×128 and used to compare CNN, RF, and SVM. CNN achieved 96% validation accuracy and RF reached 95%, while SVM with an RBF kernel surpassed them with over 98% accuracy. Evaluation relied on sensitivity, specificity, and AUC, showing low false-positive behavior for early detection and highlighting practical limits from data volume and compute resources.","Comparative Analysis of Machine Learning Models for Lung Cancer Detection Using CT Scan Images  \nMuhammad Osama1*, Ejaz Ahmed2, Misbah Batool1, Mohsin Saleem3, Ahmed Salim1 1Department of Electrical Engineering, Namal University, Mianwali, Pakistan 2Computer Science Department, Namal University, Mianwali, Pakistan  \n3Software Development Cell, Computer Science Department, Namal University, Mianwali, Pakistan  \n*[Correspondence](Correspondence: bsee23f01@namal.edu.pk)[: ](Correspondence: bsee23f01@namal.edu.pk)[bsee23f01@namal.edu.pk](Correspondence: bsee23f01@namal.edu.pk)  \nCitation | Osama. M, Ahmed. E, Batool. M, Saleem. M, Salim. A, “Comparative Analysis of Machine Learning Models for Lung Cancer Detection Using CT Scan Images”, IJIST, Special Issue pp 318-328 March 2025.  \nReceived March 02, 2025 Revised| March 14, 2025 Accepted| March 20, 2025 Published| March 23, 2025.   \nThe CT scan provides useful information but has limitations in detecting subtle patterns.  \nMachine learning models enhance cancer detection by extracting features, reducing errors, and enabling early-stage diagnosis. Unlike earlier studies that focused on single models, this paper compares three models: CNN, RF, and SVM. A total of 995 CT images were resized to 128x128 pixels, representing both healthy individuals and patients across the full range of lung cancer types. Using a feature hierarchy, CNN achieved a 96% validation accuracy, and RF reached 95%, showing robustness. However, SVM with an RBF kernel optimization outperformed the others, achieving over 98% accuracy with superior alignment of hyperplanes, particularly in detecting fine malignant patterns. The key metrics used in this study were sensitivity, specificity, and AUC, all of which showed a low false positive rate for early lung cancer detection, bridging theoretical accuracy and clinical practicality. Data volume and processing resources remain significant challenges for applying machine learning in early lung cancer diagnosis. To address these issues, we suggest hybrid architectures (e.g., CNNSVM) that combine hierarchical feature learning and hyperplane optimization. These findings  \ncould pave the way for AI-based clinical approaches, improving patient diagnosis and treatment.  \nKeywords: Lung Cancer Detection, Machine Learning Models, Ct Scan Image Analysis, Diagnostic Accuracy, Confusion Matrix  \nIntroduction:  \nBackground:  \nMedical imaging has transformed healthcare by enabling the diagnosis, monitoring, and treatment of various health conditions without surgery. Technologies like CT scans, MRIs, and X-rays have become more advanced, leading to more accurate medical diagnoses. Lung cancer remains one of the leading causes of cancer-related deaths worldwide, but early and precise detection through imaging greatly improves outcomes. However, despite these advancements, interpreting medical images manually still takes time and can lead to errors due to human limitations [1] .  \nThe approach to analyzing medical imaging data has been revolutionized. Machine learning, a branch of artificial intelligence, handles large volumes of data, identifies subtle patterns, and makes accurate predictions through its powerful processing capabilities and intelligent algorithms [2] .  \nDeep learning, a subset of machine learning, is especially effective in analyzing images. Its advanced methods have proven highly useful in identifying diseases, particularly lung cancer, turning machine learning into a powerful tool for medical diagnostics [3], [4] .  \nObjectives and Novelty:  \nThe main goals of this study are three: (1) to compare how well three machine learning models—CNN, RF, and SVM—can detect lung cancer from CT scans; (2) to enhance the methods used to identify features and classify results to help with early diagnosis and lower false positives; and (3) to carefully evaluate real-world issues (like dataset size and computing needs) when using AI-based diagnostics in hospitals.  \nThis research ","cbCaiiG2OKfDHstx","https://ap.wps.com/l/cbCaiiG2OKfDHstx","pdf",628602,1,11,"English","en",105,"# Introduction\n## Background\n## Objectives and Novelty\n## Importance of AI in Clinical Settings\n## Deep Learning in Medical Imaging","[{\"question\":\"Which machine learning models are compared for lung cancer detection?\",\"answer\":\"The study compares three models: CNN, RF, and SVM.\"},{\"question\":\"How do the models perform on validation accuracy?\",\"answer\":\"CNN reaches 96% validation accuracy and RF reaches 95%, while SVM with an RBF kernel achieves over 98% accuracy.\"},{\"question\":\"What metrics are used to evaluate diagnostic performance?\",\"answer\":\"Sensitivity, specificity, and AUC are used, with results indicating a low false positive rate for early lung cancer detection.\"}]","Comparative Analysis of Machine Learning Models for Lung Cancer Detection Using CT Scan Images | 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