[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125446-en":3,"doc-seo-125446-105":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":20,"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},125446,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Detection and Classification of Breast Cancer Calcifications Using Machine Learning with Augmentation Technique - Dissertation","This dissertation investigates automatic detection and classification of breast cancer calcifications using machine learning enhanced by an augmentation technique. The work is structured as an undergraduate medical radiation (Honours) dissertation with academic supervision, covering study background, problem statement, objectives, hypothesis, and the proposed conceptual framework. Emphasis is placed on robust modelling enabled by data augmentation, aiming to improve performance for identifying clinically relevant calcification patterns. Results and discussion support the adequacy of the scholarly approach for partial fulfilment of the degree requirement.","DETECTION AND CLASSIFICATION OF BREAST CANCER CALCIFICATIONS USING MACHINE LEARNING WITH AUGMENTATION TECHNIQUE  \nNURUL SYUHAIDA BINTI BORHANUDDIN  \nSCHOOL OF HEALTH SCIENCES UNIVERSITI SAINS MALAYSIA  \nDETECTION AND CLASSIFICATION OF BREAST CANCER CALCIFICATIONS USING MACHINE LEARNING WITH AUGMENTATION TECHNIQUE  \nby  \nNURUL SYUHAIDA BINTI BORHANUDDIN  \nDissertation submitted in partial fulfilment  \nof the requirement for the degree of Bachelor of Medical Radiation (Honours)  \nCERTIFICATE  \nThis is to certify that the dissertation entitled “DETECTION AND CLASSIFICATION OF BREAST CANCER CALCIFICATIONS USING MACHINE LEARNING WITH AUGMENTATION TECHNIQUE” is the bona fide record of research work done by NURUL SYUHAIDA BINTI BORHANUDDIN during the period from December 2024 to July 2025 under my supervision. I have read this dissertation and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation to be submitted in partial fulfilment for the degree of Bachelor of Health Science Medical Radiation (Honours) .  \nMain Supervisor:  \nMadam SitiAishah Abd Aziz Senior Lecturer  \nSchool of Health Sciences Universiti Sains Malaysia Health Campus  \n16150 Kubang Kerian Kelantan, Malaysia  \nCo-Supervisor:  \nDr Lau Chiew Chea Lecturer  \nSchool of Medical Sciences Universiti Sains Malaysia Health Campus  \n16150 Kubang Kerian Kelantan, Malaysia  \nDate: July 2025  \nDECLARARTION  \nI, Nurul Syuhaida Binti Borhanuddin, hereby declare that the dissertation entitled“DETECTION AND CLASSIFICATION OF BREAST CANCER CALCIFICATIONS USING MACHINE LEARNING WITH AUGMENTATION TECHNIQUE” is the result of my own investigations, except where otherwise stated and duly acknowledged. I also declare that it has not been previously or concurrently submitted as a whole for any other degrees at Universiti Sains Malaysia or other institutions. I grant Universiti Sains Malaysia the right to use the dissertation for teaching, research and promotional purposes.  \nNurul Syuhaida Binti Borhanuddin  \nDate: July 2025  \nACKNOWLEDGEMENT  \nIn the name of Allah, the Most Gracious and the Most Merciful. All praise be to Allah, for His blessings and guidance that have enabled me to complete this final year project as part of the requirement for the completion of my undergraduate degree.  \nFirst and foremost, I would like to express my deepest appreciation to my beloved parents, Borhanuddin bin Rabipe and Saadiah binti Sata, for their unconditional love, endless prayers, and unwavering support throughout my academic journey. Their sacrifices and encouragement have been the foundation of my strength and perseverance. I would also like to extend my heartfelt gratitude to my main supervisor, Madam Siti Aishah Abd Aziz, for her valuable guidance, continuous encouragement, and insightful supervision throughout the entire research process. My sincere thanks go to my cosupervisor, Dr. Lau Chiew Chea, for her technical expertise, thoughtful suggestions, and generous support, which greatly enhanced the quality of this research.  \nSpecial appreciation is also extended to to Dr. Muhammad Akmal Bin Remli, Director of Institute for Artificial Intelligence and Big Data (AIBIG), Universiti Malaysia Kelantan, for providing access to resources and a supportive learning environment. I am equally grateful to his dedicated PhD students, Ainin Sofia Jusoh and Meor Muhammad Muaz from AiBIG, whose assistance and knowledge sharing made complex computational tasks manageable. Lastly, I would like to thank all lecturers, friends, and colleagues who have contributed directly or indirectly to the completion of this thesis. Your encouragement and support have meant a great deal to me.  \nTABLE OF CONTENTS  \nCERTIFICATE .................................................................................................................. ii  \nDECLARARTION ..................................................................................","cbCaiq8BRXXnu7NS","https://ap.wps.com/l/cbCaiq8BRXXnu7NS","pdf",596511,1,41,"English","en",105,"# Table of Contents\n## Chapter 1\n## Introduction\n## Chapter 2\n## Literature","[{\"question\":\"What is the main topic of the dissertation?\",\"answer\":\"The dissertation focuses on detecting and classifying breast cancer calcifications using machine learning with an augmentation technique.\"},{\"question\":\"Who conducted the research and what degree is it for?\",\"answer\":\"The research is conducted by Nurul Syuhaida Binti Borhanuddin as part of a Bachelor of Medical Radiation (Honours) dissertation.\"},{\"question\":\"What sections define the study’s direction?\",\"answer\":\"The work includes study background, problem statement, objectives, hypothesis, significance of the study, and a conceptual framework.\"}]","Detection and Classification of Breast Cancer Calcifications Using Machine Learning with Augmentation Technique - 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