[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128342-en":3,"doc-seo-128342-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128342,962085564549,"Genevieve","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","EARLY-STAGE LUNG CANCER DIAGNOSIS USING NEW REGRESSION FEATURES AND MACHINE LEARNING","Lung cancer remains the most common cancer worldwide and a major cause of cancer-related mortality, largely due to late detection. Radiologists often rely on labor-intensive, error-prone visual interpretation of CT scans, where intensity variation and anatomical ambiguity can hinder accurate identification of cancerous cells. This study develops an automated early-stage lung cancer diagnosis system to support timely and reliable decision-making. The proposed pipeline covers image acquisition, preprocessing via geometrical-feature segmentation, regression-feature extraction, hybrid deep learning classification, and machine-learning evaluation aligned with patient radiology reports.","UNIVERSITI TEKNOLOGI MARA  \nEARLY-STAGE LUNG CANCER DIAGNOSIS USING NEW REGRESSION FEATURES AND MACHINE LEARNING  \nNURUL NAJIHA BINTI JAFERY  \nThesis submitted in fulfilment of the requirements for the degree of  \nDoctor of Philosophy  \n(Electrical Engineering)  \nFaculty of Electrical Engineering  \nSeptember 2025  \nABSTRACT  \nLung cancer is the most common cancer worldwide and one of the leading causes of cancer-related deaths due to late detection. Radiologists typically diagnose lung cancer through the visual analysis of computed tomography (CT) scan images, a process that is tedious, time-consuming, and prone to errors. Additionally, variations in CT scan image intensity and the potential for misinterpretation of anatomical structures make it challenging to accurately identify cancerous cells. The TNM (Tumour, Node, Metastases) staging system is commonly used by doctors and radiologists to classify lung cancer progression. Early detection of lung cancer, particularly in the T1 and T2 stages, significantly improves survival rates, highlighting the importance of timely and accurate diagnosis. This study aims to develop an automated early-stage lung cancer diagnosis system using a new regression feature extraction method and machine learning techniques. The system is designed to assist radiologists and medical experts in diagnosing lung cancer and making treatment decisions. The methodology is divided into five stages: image acquisition, pre-processing, lung lesion detection, early-stage lung cancer diagnosis, and performance evaluation. The lung CT scan images used in this study were obtained from the Advanced Medical and Dental Institute (AMDI), Universiti Sains Malaysia (USM) . In the pre-processing stage, a new segmentation method using geometrical features was proposed to segment lung lesion and non-lesion regions. For lung lesion detection, a new Regression Features (RFE) was introduced, generating four feature sets: RFE_1, RFE_2, RFE_3, and RFE_4. The best-performing set, RFE_2, was then fed into two proposed hybrid deep neural networks: Hybrid 1DCNN-LSTM and VGG16-1D-LSTM, to classify lung lesion and non-lesion regions. Both models achieved an accuracy of 96%, with the Hybrid 1D-CNN-LSTM outperforming VGG16-1D-LSTM in AUC (0.91 vs. 0.81). Identified lung lesions were further analysed in the early-stage lung cancer diagnosis stage using machine learning classifiers, including Support Vector Machine (SVM), Gradient Boosting, AdaBoost, and Random Forest. Among these, Random Forest demonstrated the highest capability for automatically diagnosing early-stage lung cancer, achieving a cross-validated accuracy of 97.14% and an AUC of 0.9884. In the performance evaluation stage, the results were correlated with patient radiology reports to assess clinical relevance. The findings suggest that the proposed system has the potential to serve as an effective decision-support tool for radiologists in diagnosing early-stage lung cancer, ultimately improving early detection, patient outcomes, and clinical workflow efficiency.  \nACKNOWLEDGEMENT  \nIn the name of Allah S.W.T, the Most Gracious, the Most Merciful. I am deeply grateful for His blessings, which have given me the opportunity to embark on my PhD and successfully complete this long and challenging journey. My deepest gratitude and thanks go to my supervisor, Assoc. Prof. Ir. Ts. Dr. Hajah Siti Noraini Sulaiman, and my co-supervisors, Assoc. Prof. Dr. Muhammad Khusairi Osman, Assoc. Prof. Dr. Noor Khairiah A. Karim, and Assoc. Prof. Ir. Ts. Dr. Zainal Hisham Che Soh. Thankyou for your continuous support, patience, and invaluable guidance in assisting me throughout this project. Your expertise, availability, and encouragement have been critical to the success of this research. I sincerely appreciate your willingness to share both my moments of joy and my moments of frustration.  \nI would also like to express my gratitude to the staff of the Advanced Medical and Dental Institute (","cbCaih3pD50qd29N","https://ap.wps.com/l/cbCaih3pD50qd29N","pdf",219452,3,1,5,"English","en",105,"# CHAPTER 1 INTRODUCTION\n## 1.1 Research Background\n## 1.2 Problem Statement\n## 1.3 Objectives\n## 1.4 Research Scope\n## 1.5 Thesis Layout\n# CHAPTER 2 LITERATURE REVIEW\n## 2.1 Introduction","[{\"question\":\"Why is early-stage lung cancer diagnosis important in this research?\",\"answer\":\"Early detection, especially at T1 and T2 stages, improves survival rates. The study targets timely and accurate diagnosis to support better outcomes.\"},{\"question\":\"What is the proposed system’s core workflow?\",\"answer\":\"The workflow includes image acquisition, preprocessing with a geometrical-feature segmentation method, regression feature extraction for lung lesion detection, hybrid deep neural network classification, and performance evaluation.\"},{\"question\":\"Which models and features achieved the best performance?\",\"answer\":\"For lesion/non-lesion classification, the RFE_2 feature set was used, and both hybrid models reached 96% accuracy, with Hybrid 1D-CNN-LSTM higher in AUC (0.91 vs. 0.81). For early-stage diagnosis, Random Forest achieved the highest capability with cross-validated accuracy of 97.14% and AUC of 0.9884.\"}]","EARLY-STAGE LUNG CANCER DIAGNOSIS USING NEW REGRESSION FEATURES AND MACHINE LEARNING | PDF",1785946945,13,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"early-stage-lung-cancer-diagnosis-using-new-regression-features-and-machine-learning","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/early-stage-lung-cancer-diagnosis-using-new-regression-features-and-machine-learning/128342/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-28","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is early-stage lung cancer diagnosis important in this research?","Question",{"text":76,"@type":77},"Early detection, especially at T1 and T2 stages, improves survival rates. The study targets timely and accurate diagnosis to support better outcomes.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What is the proposed system’s core workflow?",{"text":81,"@type":77},"The workflow includes image acquisition, preprocessing with a geometrical-feature segmentation method, regression feature extraction for lung lesion detection, hybrid deep neural network classification, and performance evaluation.",{"name":83,"@type":74,"acceptedAnswer":84},"Which models and features achieved the best performance?",{"text":85,"@type":77},"For lesion/non-lesion classification, the RFE_2 feature set was used, and both hybrid models reached 96% accuracy, with Hybrid 1D-CNN-LSTM higher in AUC (0.91 vs. 0.81). 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