[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121099-en":3,"doc-seo-121099-105":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},121099,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Enhancing Medical Diagnosis Through Deep Learning and Machine Learning Approaches in Image Analysis","Medical imaging analysis plays a critical role in transforming disease discovery, diagnosis, and treatment. By integrating machine learning and deep learning, the field has accelerated the development of advanced algorithms that improve diagnostic accuracy and disease detection. This study evaluates the impact of state-of-the-art techniques on accuracy, and examines how different medical imaging types affect algorithm performance and efficiency, while addressing implementation limitations. The work also explores how constraints influence healthcare professionals’ decisions and outlines practical applications of recent advances in medical image analysis.","Enhancing Medical Diagnosis Through Deep Learning and Machine Learning  \nApproaches in Image Analysis  \nUsmani Usman Ahmad, Happonen Ari, Watada Junzo  \nThis is a Author's accepted manuscript (AAM) version of a publication published by Springer, Cham  \nin Intelligent Systems and Applications. IntelliSys 2023. Lecture Notes in Networks and Systems, vol 825  \nDOI: 10. 1007/978-3-031-47718-8_30  \nCopyright of the original publication:  \n© Springer, Cham 2024  \nPlease cite the publication as follows:  \nUsmani, U.A. , Happonen, A. , Watada, J. (2024) . Enhancing Medical Diagnosis Through Deep Learning and Machine Learning Approaches in Image Analysis. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2023 . Lecture Notes in Networks and Systems, vol 825 . Springer, Cham. [https://doi.org/10.1007/978-3-031-47718-8_30](https://doi.org/10.1007/978-3-031-47718-8_30)  \nThis is a parallel published version of an original publication. This version can differ from the original published article.  \nEnhancing Medical Diagnosis Through Deep Learning and Machine Learning Approaches in Image Analysis  \nUsman Ahmad Usmani1, Ari Happonen2 , and Junzo Watada3  \n1 Universiti Teknologi Petronas, 79 LakeVille Seri Iskandar 32610, Perak, Malaysia  \n2 LUT University, Yliopistonkatu 34, 53850 Lappeenranta, Finland  \n3 1 Chome-104 Totsukamachi, Shinjuku, Tokyo 169-8050, Japan  \nAbstract. Medical imaging analysis plays a critical role in the medical field, transforming how diseases are found, diagnosed, and treated. The integration of machine learning and deep learning has dramatically advanced thefield ofmedical image analysis, leading to the creation of more advanced algorithms for improved diagnosis and disease detection. This study examines the impact of these cuttingedge technologies on the accuracy of medical imaging analysis. It investigates the most effective algorithms and techniques currently used, as well as how different types of medical images impact the accuracy and efficiency of these algorithms.  \nThe limitations and challenges faced during implementation and their effect on healthcare professionals’ decision-making are also explored. This research providesa comprehensive understanding ofthe state oftheart in medical image analysis through machine learning and deep learning, highlighting recent developmentsand their practical applications.  \nKeywords: Medical diagnosis · Image analysis · Radiology · Pathology ·  \nMachine learning · Computer-Aided diagnosis · Deep learning · Artificial intelligence · Imaging modalities · Digitalizatio · Ethical data analysis · Smart society  \n1 Introduction  \nMedical image analysis, like all other industry-enhancing digitalization solutions nowadays [4, 7], play a vital role in the medical industry, particularly in diagnosing and treating illnesses. It involves various imaging techniques, such as X-rays, CT scans, MRI scans, and ultrasounds, to create images of the human body’s internal structures and organs. [1] For many years, healthcare professionals primarily handled the interpretation of these images. However, with the advancement of technology and the introduction of artificial intelligence (AI), the field of medical image analysis area and tools can undergo significant changes, transforming from an asset to a knowledge era [2] . The algorithms can  \nalso segment and divide medical images into different regions of interest based on the presence of specific structures or tissues [8, 75, 78] . ML is a part ofAI that concentrateson developing algorithms learning, and predicting as a data-based process [3, 6, 76, 82] . The combination of ML and DL has resulted in the creation of algorithms providing support in diagnosing and detecting illnesses, as is shown by the results presented in this study on the impact of ML and DL techniques with their current challenges and limitations. With challenges in mind, it will be essential to consider why people participate and what motivates them to join into","cbCaisj7c8Df3Qvt","https://ap.wps.com/l/cbCaisj7c8Df3Qvt","pdf",790726,1,21,"English","en",105,"# Abstract\n# Introduction\n## Medical image analysis and digitalization\n## ML/DL roles in classification and segmentation\n## Challenges, limitations, and future development","[{\"question\":\"How do machine learning and deep learning improve medical imaging diagnosis?\",\"answer\":\"They enable advanced algorithms that support classification and detection, improving diagnostic accuracy and disease identification compared with more traditional workflows.\"},{\"question\":\"Which medical image types are considered, and why do they matter?\",\"answer\":\"The study examines how different imaging modalities influence algorithm accuracy and efficiency, noting that variability in images and diseases affects performance.\"},{\"question\":\"What challenges arise when implementing ML/DL in medical image analysis?\",\"answer\":\"The document highlights limitations related to medical-image complexity, disease variability, and general AI implementation issues, and discusses how these limitations can affect healthcare professionals’ decision-making.\"}]","Enhancing Medical Diagnosis Through Deep Learning and Machine Learning Approaches in Image Analysis | 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do machine learning and deep learning improve medical imaging diagnosis?","Question",{"text":75,"@type":76},"They enable advanced algorithms that support classification and detection, improving diagnostic accuracy and disease identification compared with more traditional workflows.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which medical image types are considered, and why do they matter?",{"text":80,"@type":76},"The study examines how different imaging modalities influence algorithm accuracy and efficiency, noting that variability in images and diseases affects performance.",{"name":82,"@type":73,"acceptedAnswer":83},"What challenges arise when implementing ML/DL in medical image analysis?",{"text":84,"@type":76},"The document highlights limitations related to medical-image complexity, disease variability, and general AI implementation issues, and discusses how these limitations can affect healthcare professionals’ 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