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Eliwa 1,2, *, Amr Mohamed El Koshiry 3,4, Tarek Abd El-Hafeez 2,5, * and Ahmed Omar 2, *  \nCitation: Eliwa, E.H.I.; Mohamed El Koshiry, A.; Abd El-Hafeez, T.; Omar, A. Secure and Transparent Lung and Colon Cancer Classification Using Blockchain and Microsoft Azure. Adv. Respir. Med. 2024, 92, 395–420 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)arm92050037  \nAcademic Editor: Sebastian Majewski  \nReceived: 11 September 2024  \nRevised: 7 October 2024  \nAccepted: 15 October 2024  \nPublished: 17 October 2024  \nCopyright: © 2024 by the authors. Published by MDPI on behalf of the Polish Respiratory Society. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Mathematics and Statistics, College of Science, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi Arabia  \n2 Department of Computer Science, Faculty of Science, Minia University, El Minia 61519, Egypt  \n3 Department of Curricula and Teaching Methods, College of Education, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi Arabia; [aalkoshiry@kfu.edu.sa](aalkoshiry@kfu.edu.sa)  \n4 Faculty of Specific Education, Minia University, El-Minia 61519, Egypt  \n5 Computer Science Unit, Deraya University, El-Minia 61765, Egypt  \n* Correspondence: [eheliwa@kfu.edu.sa](eheliwa@kfu.edu.sa) (E.H.I.E.); [tarek@mu.edu.eg](tarek@mu.edu.eg) (T.A.E.-H.); [ahmed.omar@mu.edu.eg](ahmed.omar@mu.edu.eg) (A.O.)  \nHighlights:  \nThe study presents a novel framework for remote consultation and lung and colon cancer classification, leveraging blockchain technology and Microsoft Azure cloud services to ensure data privacy and security. The proposed framework achieves an impressive accuracy of 100% for lung and colon cancer classification using advanced machine learning models, demonstrating its potential to improve diagnostic accuracy and streamline cancer care.  \nWhat are the main findings?  \n• Effective Cancer Classification: The framework effectively classifies lung and colon cancer using state-of-the-art machine learning models, achieving high accuracy, precision, recall, and F1-score.  \n• Enhanced Data Security: Blockchain technology and Microsoft Azure cloud services provide a secure and transparent environment for data storage, access, and sharing, ensuring patient privacy and data integrity.  \nWhat is the implication of the main finding?  \n• Improved Diagnostic Efficiency: The proposed framework has the potential to significantly improve the efficiency of lung and colon cancer diagnosis by enabling remote consultations and providing accurate and timely results.  \n• Enhanced Patient Outcomes: By improving diagnostic accuracy and streamlining the cancer care process, this framework can contribute to better patient outcomes and reduce the overall burden of lung and colon cancers.  \nAbstract: Background: The global healthcare system faces challenges in diagnosing and managing lung and colon cancers, which are significant health burdens. Traditional diagnostic methods are inefficient and prone to errors, while data privacy and security concerns persist. Objective: This study aims to develop a secure and transparent framework for remote consultation and classification of lung and colon cancer, leveraging blockchain technology and Microsoft Azure cloud services. Dataset and Features: The framework utilizes the LC25000 dataset, containing 25,000 histopathological images, for training and evaluating advanced machine learning models. Key features include secure data upload, anonymization, encryption, and controlled access via blockchain and Azure se","cbCailLda7rDo0bf","https://ap.wps.com/l/cbCailLda7rDo0bf","pdf",4383872,26,"English","# Introduction\n# Proposed Framework\n## Data Upload, Anonymization, and Storage\n## Blockchain Smart Contracts and Access Control\n## Azure Machine Learning for Classification\n# Evaluation Metrics\n# Results and Discussion\n# Conclusions","[{\"question\":\"What classification performance is reported and how is it evaluated?\",\"answer\":\"The framework uses DenseNet, ResNet50, and MobileNet models across split ratios (70–30, 80–20, 90–10) and reports accuracy up to 100%, with high F1-scores and k-fold cross-validation results (5-fold and 10-fold) using metrics such as accuracy, precision, recall, and F1-score.\"}]","Secure and Transparent Lung and Colon Cancer Classification Using Blockchain and Microsoft Azure - Research Summary | PDF",1789993302,66]