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AAL with Deep Learning to Classify the Diseases Remotely from the Image Data

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Enabling Person-Centric Healthcare Using Ambient Assistive Technology

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1108))

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Abstract

In the preceding decennium, Deployment of ambient assistive living technology to promote self-dependent life is keep on intensifying [1]. The populace and the divergence of inherent features on the way to an aged population results in incorporating unfamiliar provocations to the current habitants from each of two a remunerative and communal perspective. Ambient Assistive Living technology can be able to proffer a bunch of clarifications for refining the fineness of survival of mankind, permitting personnel to stay finer and unaccompanied for long time, to assist the people possess disorders, and the subsisting mechanization proffers enormous assistance for caregivers, the proposed technology offers immense support for caretakers and medical subordinates. An extensive investigation is demonstrated to label the prime fashion towards the blossoming of Ambient Assistive Living technology and its requirement for self-dependent living [2]. The ambient technology incorporates deep learning techniques [3,4,5,6,7,8] to scrutinize the information gathered by the system and to eliminate the requirement of superior’s suggestions.

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References

  1. Jovanovic, M., Mitrov, G., Zdravevski, E., Lameski, P., Colantonio, S., Kampel, M., Tellioglu, H., & Florez- Revuelta, F. (2022). Ambient assisted living: Scoping review of artificial intelligence models, domains, technology, and concerns. Journal of Medical Internet Research, 24(11), e36553. https://doi.org/10.2196/36553

    Article  Google Scholar 

  2. Gams, M., Gu, I. Y.-H., Härmä, A., Muñoz, A., & Tam, V. (2019). Artificial intelligence and ambient intelligence. Journal of Ambient Intelligence and Smart Environments, 11(1), 71–86. https://doi.org/10.3233/AIS-180508

    Article  Google Scholar 

  3. Dai, Y., Shen, L., Cao, Y., Lei, T., & Qiao, W. (2019). Detection of vegetation areas attacked by pests and diseases based on adaptively weighted enhanced global and local deep features. IGARSS 2019—2019 IEEE International Geoscience and Remote Sensing Symposium, 6495–6498. https://doi.org/10.1109/IGARSS.2019.8898517

  4. Marefat, M., & Juneja, A. (2019). Serverless data parallelization for training and retraining of deep learning architecture in patient-specific arrhythmia detection. IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), 1–4. https://doi.org/10.1109/BHI.2019.8834566

    Article  Google Scholar 

  5. Shu, M. (2019). Deep learning for image classification on very small datasets using transfer learning. Creative Components. 345. https://lib.dr.iastate.edu/creativecomponents/345

  6. Shaheen, M., Khan, R., Biswal, R. R., Ullah, M., Khan, A., Uddin, M. I., Zareei, M., & Waheed, A. (2021). Acute Myeloid Leukemia (AML) detection using AlexNet model. Complexity, 2021(Article ID 6658192), 8. https://doi.org/10.1155/2021/6658192

  7. Agarwal, A., Patni, K., & Rajeswari, D. (2021). Lung cancer detection and classification based on Alexnet CNN. 2021 6th International Conference on Communication and Electronics Systems (ICCES), 1390–1397. https://doi.org/10.1109/ICCES51350.2021.9489033

  8. Alkafrawi, I. M. I., & Dakhell, Z. A. (2022). Blood cells classification using deep learning technique. International Conference on Engineering & MIS (ICEMIS), 1–6. https://doi.org/10.1109/ICEMIS56295.2022.9914281

    Article  Google Scholar 

  9. Bastaki, B. B., Bosakowski, T., & Benkhelifa, E. (2017). Intelligent assisted living framework for monitoring elders. 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA), 495–500. https://doi.org/10.1109/AICCSA.2017.213

  10. Ziefle, M., Rocker, C., & Holzinger, A. (2011). Perceived usefulness of assistive technologies and Electronic services for ambient assisted living. 2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops, 585–592. https://doi.org/10.4108/icst.pervasivehealth.2011.246044

  11. Hartanto, C. A., & Wibowo, A. (2020). Development of mobile skin cancer detection using faster R-CNN and MobileNet v2 model. 2020 7th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), 58–63. https://doi.org/10.1109/ICITACEE50144.2020.9239197

  12. Saha, S., Bhadra, R., & Kar, S. (2021). Diagnosis of COVID-19 & Pneumonia from Chest x-ray Scans using modified MobileNet architecture. 2021 IEEE Mysore Sub Section International Conference (MysuruCon), 793–798. https://doi.org/10.1109/MysuruCon52639.2021.9641739

  13. Naga Srinivasu, P., JayaLakshmi, G., Jhaveri, R. H., & Praveen, S. P. (2022). Ambient assistive living for monitoring the physical activity of diabetic adults through body area networks. Mobile Information Systems, Article ID 3169927. https://doi.org/10.1155/2022/3169927

  14. Parvin, P., Paternó, F., & Chessa, S. (2018, June 25–28). Anomaly detection in the elderly daily behavior. In Proceedings of the 14th International Conference on Intelligent Environments. IEEE.

    Google Scholar 

  15. Forkan, A., Khalil, I., & Tari, Z. (2014). CoCaMAAL: A cloud-oriented context-aware middleware in ambient assisted living. Future Generation Computer Systems, 35, 114–127.

    Article  Google Scholar 

  16. Yang, Y., Gu, H., Han, Y., & Li, H. (2020). An end-to-end deep learning change detection framework for remote sensing images. IGARSS 2020—2020 IEEE International Geoscience and Remote Sensing Symposium, 652–655. https://doi.org/10.1109/IGARSS39084.2020.9324076

  17. Latha, R. S., Sreekanth, G. R. R., Suganthe, R. C., & Selvaraj, R. E. (2021). A survey on the applications of Deep Neural Networks. 2021 International Conference on Computer Communication and Informatics (ICCCI), 1–3. https://doi.org/10.1109/ICCCI50826.2021.9457016

    Article  Google Scholar 

  18. Valarmathi, S., & Vijayabhanu, R. (2021). A survey on diabetic retinopathy disease detection and classification using deep learning techniques. 2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII), 1–4. https://doi.org/10.1109/ICBSII51839.2021.9445163

  19. Muhammad, K., Khan, S., Ser, J. D., & Albuquerque, V. H. C. d. (2021, February). Deep learning for multigrade brain tumor classification in smart healthcare systems: A prospective survey. IEEE Transactions on Neural Networks and Learning Systems, 32(2), 507–522. https://doi.org/10.1109/TNNLS.2020.2995800

  20. He, Z. (2020). Deep learning in image classification: A survey report. 2020 2nd International Conference on Information Technology and Computer Application (ITCA), 174–177. https://doi.org/10.1109/ITCA52113.2020.00043

  21. Aziz, S., Bilal, M., Khan, M. U., & Amjad, F. (2020). Deep learning-based automatic morphological classification of leukocytes using blood smears. 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), 1–5. https://doi.org/10.1109/ICECCE49384.2020.9179246

  22. Al-Adhaileh, M. H. (2021). Diagnosis and classication of Alzheimer’s disease by using a convolution neural network algorithm. 2021 Soft Computing. https://doi.org/10.21203/rs.3.rs-1021353/v1

  23. Thanzeem Mohamed Sheriff, S., Venkat Kumar, J., Vigneshwaran, S., Jones, A., & Anand, J. (2021). Lung cancer detection using VGG NET 16 architecture. International Conference on Physics and Energy 2021 (ICPAE 2021). https://doi.org/10.1088/1742-6596/2040/1/012001

  24. Zakaria, N., Mohamed, F., Abdelghani, R., & Sundaraj, K. (2021). Three ResNet deep learning architectures applied in pulmonary pathologies classification. 2021 International Conference on Artificial Intelligence for Cyber Security Systems and Privacy (AI-CSP), 1–8. https://doi.org/10.1109/AI-CSP52968.2021.9671211

  25. Budhiman, A., Suyanto, S., & Arifianto, A. (2019). Melanoma cancer classification using ResNet with data augmentation. 2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), 17–20. https://doi.org/10.1109/ISRITI48646.2019.9034624

  26. Sirco, A., Almisreb, A., Tahir, N. M., & Bakri, J. (2022). Liver tumour segmentation based on ResNet technique. 2022 IEEE 12th International Conference on Control System, Computing and Engineering (ICCSCE), 203–208. https://doi.org/10.1109/ICCSCE54767.2022.9935636

  27. Praveen, S. P., Jyothi, V. E., Anuradha, C., VenuGopal, K., Shariff, V., & Sindhura, S. (2022). Chronic kidney disease prediction using ML-based Neuro-Fuzzy model. International Journal of Image and Graphics, 2340013. https://doi.org/10.1142/S0219467823400132

  28. Zheng, Z., Zhang, H., Li, X., Liu, S., & Teng, Y. (2021). ResNet-based model for cancer detection. 2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE), 325–328. https://doi.org/10.1109/ICCECE51280.2021.9342346

  29. Bing-jin, L., Jian, Y., Yan-jun, L., Liang, P., & Guo-xiong, L. (2020). Research and practice of X-ray chest film disease classification based on DenseNet. 2020 International Conference on Artificial Intelligence and Education (ICAIE), 241–244. https://doi.org/10.1109/ICAIE50891.2020.00063

  30. Lalitha, V., Raghul, G., & Premkumar, A. R. (2020). Leukocyte counting and reporting using densenet deep learning. 2020 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS), 1–6. https://doi.org/10.1109/ICPECTS49113.2020.9337022

  31. Wang, Q., Yang, B., Liu, W., & Chen, G. (2021). X-ray images detection of COVID-19 based on deepwise separable DenseNet. 2021 IEEE 6th International Conference on Signal and Image Processing (ICSIP), 294–298. https://doi.org/10.1109/ICSIP52628.2021.9688876

  32. Tiwari, R., Verma, M., & Sar, S. K. (2022). Detecting different thoracic disease using CNN-model. 2022 International Conference for Advancement in Technology (ICONAT), 1–11. https://doi.org/10.1109/ICONAT53423.2022.9725940

  33. He, G., Ping, A., Wang, X., & Zhu, Y. (2019). Alzheimer’s disease diagnosis model based on three dimensional full convolutional DenseNet. 2019 10th International Conference on Information Technology in Medicine and Education (ITME), 13–17. https://doi.org/10.1109/ITME.2019.00014

  34. Ovreiu, S., Paraschiv, E.-A., & Ovreiu, E. (2021). Deep learning & digital fundus images: Glaucoma detection using DenseNet. 2021 13th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), 1–4. https://doi.org/10.1109/ECAI52376.2021.9515188

  35. Naga Srinivasu, P., Krishna, T. B., Ahmed, S., Almusallam, N., Khaled Alarfaj, F., & Allheeib, N. (2023). January 17). Variational autoencoders-basedself-learning model for tumor identification and impact analysis from 2-D MRI images. Journal of Healthcare Engineering, 2023, 1–17. https://doi.org/10.1155/2023/1566123

  36. Verma, S., Razzaque, M. A., Sangtongdee, U., Arpnikanondt, C., Tassaneetrithep, B., & Hossain, A. (2021). Digital diagnosis of hand, foot, and mouth disease using hybrid deep neural networks. IEEE Access, 9, 143481–143494. https://doi.org/10.1109/ACCESS.2021.3120199

    Article  Google Scholar 

  37. Francis, A., & Pandian, I. A. (2021). Early detection of Alzheimer’s disease using ensemble of pre-trained models. 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), 692–696. https://doi.org/10.1109/ICAIS50930.2021.9395988

  38. Rumala, D. J., et al. (2021). Bilinear MobileNets for multi-class brain disease classification based on magnetic resonance images. 2021 IEEE Region 10 Symposium (TENSYMP), 1–6. https://doi.org/10.1109/TENSYMP52854.2021.9550987

  39. Ahmed, S., Srinivasu, P., Alhumam, A., & Alarfaj, M. (2022, November 9). AAL and internet of medical things for monitoring type-2 diabetic patients. Diagnostics, 12(11), 2739. https://doi.org/10.3390/diagnostics12112739

  40. Prawira, R., Bustamam, A., & Anki, P. (2021). Multi label classification of retinal disease on fundus images using AlexNet and VGG16 architectures. 2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), 464–468. https://doi.org/10.1109/ISRITI54043.2021.9702817

  41. Badgujar, S., & Pillai, A. S. (2020, July 1–3). Fall detection for elderly people using machine learning. In Proceedings of the 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE.

    Google Scholar 

  42. Sarabia, D., Usach, R., Palau, C., & Esteve, M. (2020). Highly-efficient fog-based deep learning AAL fall detection system. Internet Things, 11, 100185. https://doi.org/10.1016/j.iot.2020.100185

    Article  Google Scholar 

  43. Srinivasu, P. N., Bhoi, A. K., Jhaveri, R. H., Reddy, G. T., & Bilal, M. (2021, July 17). Probabilistic deep Q network for real-time path planning in censorious robotic procedures using force sensors. Journal of Real-Time Image Processing, 18(5), 1773–1785. https://doi.org/10.1007/s11554-021-01122-x

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Author information

Authors and Affiliations

  1. Department of Electronics and Communication, Bannari Amman Institute of Technology, Erode, India

    A. Sharmila & A. Sharmila

  2. Department of Electronics and Communication, Erode Sengunthar Engineering College, Erode, India

    E. L. Dhivya Priya

  3. Department of Electronics and Communication, KPR Institute of Engineering and Technology, Coimbatore, India

    K. S. Tamilselvan

  4. Department of Electronics and Communication, Dr Mahalingam College of Engineering and Technology, Coimbatore, India

    K. R. Gokul Anand

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  1. A. Sharmila
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  2. E. L. Dhivya Priya
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  3. K. S. Tamilselvan
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  4. K. R. Gokul Anand
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Corresponding author

Correspondence to A. Sharmila .

Editor information

Editors and Affiliations

  1. National Research Council, Institute of Information Science and Technologies, Pisa, Italy

    Paolo Barsocchi

  2. Department of Computer Science and Engineering, Prasad V Potluri Siddhartha Institute of Technology, Vijayawada, India

    Naga Srinivasu Parvathaneni

  3. KIET Group of Institutions, Ghaziabad, India

    Amik Garg

  4. KIET Group of Institutions, Ghaziabad, India

    Akash Kumar Bhoi

  5. National Research Council, Institute of Information Science and Technologies, Pisa, Italy

    Filippo Palumbo

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Sharmila, A., Priya, E.L.D., Tamilselvan, K.S., Anand, K.R.G. (2023). AAL with Deep Learning to Classify the Diseases Remotely from the Image Data. In: Barsocchi, P., Parvathaneni, N.S., Garg, A., Bhoi, A.K., Palumbo, F. (eds) Enabling Person-Centric Healthcare Using Ambient Assistive Technology. Studies in Computational Intelligence, vol 1108. Springer, Cham. https://doi.org/10.1007/978-3-031-38281-9_5

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