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A machine learningbased framework to Classify Xrays anomality

Published: 13 May 2024 Publication History

Abstract

In recent years, the intersection of healthcare and technology has witnessed transformative advancements, particularly in medical imaging. The integration of machine learning techniques has empowered the medical community to enhance diagnostic accuracy and streamline healthcare processes. This paper introduces a comprehensive machine learning-based framework designed for classifying X-ray anomalies within the dynamic and scalable environment of cloud computing. The motivation behind this framework stems from the need for scalable, accurate, and cost-effective solutions in healthcare. Leveraging cloud computing's elastic resources, our framework aims to accelerate the deployment of machine learning models for X-ray anomaly classification, ensuring accessibility and affordability across diverse healthcare settings. Gather a diverse dataset of X-ray images with both normal and abnormal cases. Ensure a balanced representation of different anomalies. Utilize the validation set to fine-tune hyperparameters and prevent overfitting. Develop a robust machine learning model capable of accurately classifying X-ray anomalies, leveraging advanced convolutional neural networks (CNNs) and transfer learning techniques. The hardware and software required are discussed in relation to the functioning and integration of Cloud Computing and Blockchain. Then we have gone through the model of machine learning for detection of anomality in X-rays(infection) step by step and in detail. The model discussed in the research paper can be of great importance to society as many people in society lose their lives to pneumonia every year. With the help of image classification, we can detect the areas of lung infected with infection, hence helping doctors in swift treatment of the patients.

References

[1]
Saban, M., Bekkour, M., Amdaouch, I., El Gueri, J., Ait Ahmed, B., Chaari, M. Z., ... & Aghzout, O. (2023). A Smart Agricultural System Based on PLC and a Cloud Computing Web Application Using LoRa and LoRaWan. Sensors, 23(5), 2725.
[2]
Gill, S. S., Fuller, S., Cabral, A., Chen, Y., & Uhlig, S. (2023). Curriculum Redesign for Cloud Computing to Enhance Social Justice and Intercultural Development in Higher Education. In Handbook of Research on Fostering Social Justice Through Intercultural and Multilingual Communication (pp. 62-80). IGI Global.
[3]
Srivastava, P. K., Kumar, S., Tiwari, A., Goyal, D., & Mamodiya, U. (2023, June). Internet of thing uses in materialistic ameliorate farming through AI. In AIP Conference Proceedings (Vol. 2782, No. 1). AIP Publishing.
[4]
Katal, A., Dahiya, S., & Choudhury, T. (2023). Energy efficiency in cloud computing data centers: a survey on software technologies. Cluster Computing, 26(3), 1845-1875.
[5]
Dora Pravina, C. T., Buradkar, M. U., Jamal, M. K., Tiwari, A., Mamodiya, U., & Goyal, D. (2022, December). A Sustainable and Secure Cloud resource provisioning system in Industrial Internet of Things (IIoT) based on Image Encryption. In Proceedings of the 4th International Conference on Information Management & Machine Intelligence (pp. 1-5).
[6]
Upadhyay, R. K. (2023). Reasearch in Cloud Computing Security. IRJMETS, 5, 2582-5208.
[7]
Rohinidevi, V. V., Srivastava, P. K., Dubey, N., Tiwari, S., & Tiwari, A. (2022, December). A Taxonomy towards fog computing Resource Allocation. In 2022 2nd International Conference on Innovative Sustainable Computational Technologies (CISCT) (pp. 1-5). IEEE.
[8]
Singh, N. K., Jain, A., Arya, S., Gonzales, W. E. G., Flores, J. E. A., & Tiwari, A. (2022, December). Attack Detection Taxonomy System in cloud services. In 2022 2nd International Conference on Innovative Sustainable Computational Technologies (CISCT) (pp. 1-5). IEEE.
[9]
Chouhan, A., Tiwari, A., Diwaker, C., & Sharma, A. (2022, February). Efficient Opportunities and Boundaries towards Internet of Things (IoT) Cost Adaptive Model. In 2022 IEEE Delhi Section Conference (DELCON) (pp. 1-5). IEEE.
[10]
Rathore, H., Sahay, S. K., Nikam, P., & Sewak, M. (2021). Robust android malware detection system against adversarial attacks using q-learning. Information Systems Frontiers, 23, 867-882.
[11]
Saxe, J., & Berlin, K. (2015, October). Deep neural network based malware detection using two dimensional binary program features. In 2015 10th international conference on malicious and unwanted software (MALWARE) (pp. 11-20). IEEE.
[12]
Tiwari, Ashish, and Ritu Garg. "ACCOS: A Hybrid Anomaly-Aware Cloud Computing Formulation-Based Ontology Services in Clouds." In ISIC, pp. 341-346. 2021.
[13]
Tiwari, A. & Sharma, R. M. (2021). Realm Towards Service Optimization in Fog Computing. In I. Management Association (Ed.), Research Anthology on Architectures, Frameworks, and Integration Strategies for Distributed and Cloud Computing (pp. 1530-1563).
[14]
Dudeja, D., Sabharwal, S. M., Ganganwar, Y., Singhal, M., Goyal, N., & Tiwari, A. (2023). Sales-Based Models for Resource Management and Scheduling in Artificial Intelligence Systems. Engineering Proceedings, 59(1), 43.
[15]
Kumar, M. (2023). Scalable malware detection system using distributed deep learning. Cybernetics and Systems, 54(5), 619-647.
[16]
Tiwari, A., Mahrishi, M., & Fatehpuria, S. (2014). A Broking Structure Originated on Service accommodative Using MROSP Algorithm.
[17]
Hemalatha, J., Roseline, S. A., Geetha, S., Kadry, S., & Damaševičius, R. (2021). An efficient densenet-based deep learning model for malware detection. Entropy, 23(3), 344.
[18]
Tiwari, A. Dr. A. Nagaraju and Mehul Mahrishi, An Optimized Scheduling Algorithm for Cloud Broker Using Cost Adaptive Modeling. In 3rd IEEE (International Advanced Computing Conference-2013).
[19]
Fatima, A., Kumar, S., & Dutta, M. K. (2021). Host-server-based malware detection system for android platforms using machine learning. In Advances in Computational Intelligence and Communication Technology: Proceedings of CICT 2019 (pp. 195-205). Springer Singapore.
[20]
Kumar Sharma, A., Tiwari, A., Bohra, B., & Khan, S. (2018). A Vision towards Optimization of Ontological Datacenters Computing World. International Journal of Information Systems & Management Science, 1(2).
[21]
Chikersal, P., Doryab, A., Tumminia, M., Villalba, D. K., Dutcher, J. M., Liu, X., ... & Dey, A. K. (2021). Detecting depression and predicting its onset using longitudinal symptoms captured by passive sensing: a machine learning approach with robust feature selection. ACM Transactions on Computer-Human Interaction (TOCHI), 28(1), 1-41.
[22]
Tiwari, A., Sharma, R. M., & Garg, R. (2020). Emerging ontology formulation of optimized internet of things (IOT) services with cloud computing. In Soft Computing: Theories and Applications: Proceedings of SoCTA 2018 (pp. 31-52). Springer Singapore.
[23]
Tiwari, A., Sah, M. K., & Malhotra, A. (2015, September). Effective service Utilization in Cloud Computing exploitation victimisation rough pure mathematics as revised ROSP. In 2015 4th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO)(Trends and Future Directions) (pp. 1-6). IEEE
[24]
Tiwari, A., & Sharma, R. M. (2021). OCC: a hybrid multiprocessing computing service decision making using ontology system. International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), 16(4), 96-116.
[25]
Sah, M. K., Kumar, V., & Tiwari, A. (2017). Security and concurrency control in distributed database system. International Journal of scientific research and management, 2(12), 1839-1845
[26]
Tiwari, A., Kumar, S., Baishwar, N., Vishwakarma, S. K., & Singh, P. (2022, September). Efficient Cloud Orchestration Services in Computing. In Proceedings of 3rd International Conference on Machine Learning, Advances in Computing, Renewable Energy and Communication: MARC 2021 (pp. 739-746). Singapore: Springer Nature Singapore.
[27]
Tiwari, A., & Sharma, R. M. (2018). Rendering Form Ontology Methodology for IoT Services in Cloud Computing. International Journal of Advanced Studies of Scientific Research, 3(11)
[28]
Mail, M. A. E., Ab Razak, M. F., & Ab Rahman, M. (2022). Malware detection system using cloud sandbox, machine learning. International Journal of Software Engineering and Computer Systems, 8(2), 25-32.
[29]
Rangaiah, Y. V., Sharma, A. K., Bhargavi, T., Chopra, M., Mahapatra, C., & Tiwari, A. (2022, December). A Taxonomy towards Blockchain based Multimedia content Security. In 2022 2nd International Conference on Innovative Sustainable Computational Technologies (CISCT) (pp. 1-4). IEEE.
[30]
Tiwari, A., & Garg, R. (2022). A Optimized Taxonomy on Spot Sale Services Using Mathematical Methodology. International Journal of Security and Privacy in Pervasive Computing (IJSPPC), 14(1), 1-21.
[31]
Tiwari, A., & Garg, R. (2019). Eagle Techniques In Cloud Computational Formulation. International Journal of Innovative Technology and Exploring Engineering, 8(1), 422-429.
[32]
Pawar, S., & Singh, S. (2015). Performance comparison of VMWare and Xen hypervisor on guest OS. Int. J. Innov. Comput. Sci. Eng. Issue, 2(3), 56-60.
[33]
Nishad, L. S., Akriti, Paliwal, J., Pandey, R., Beniwal, S., & Kumar, S. (2016, March). Security, privacy issues and challenges in cloud computing: A survey. In Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies (pp. 1-7).
[34]
Sahu, K., Srivastava, R. K., Kumar, S., Saxena, M., Gupta, B. K., & Verma, R. P. (2023). Integrated hesitant fuzzy-based decision-making framework for evaluating sustainable and renewable energy. International Journal of Data Science and Analytics, 16(3), 371-390.
[35]
Koppaiyan, R. S., Pallivalappil, A. S., Singh, P., Tabassum, H., Tewari, P., Sweeti, M., & Kumar, S. (2022, December). High-Availability Encryption-Based Cloud Resource Provisioning System. In Proceedings of the 4th International Conference on Information Management & Machine Intelligence (pp. 1-6).
[36]
An approach to reduce turn around time and waiting time by the selection of round robin and shortest job first algorithm

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ICIMMI '23: Proceedings of the 5th International Conference on Information Management & Machine Intelligence
November 2023
1215 pages
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Published: 13 May 2024

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Author Tags

  1. AI
  2. Challenges Machine Learning
  3. Cloud Computing
  4. Security Mechanisms

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