Indian Journal of Computer Science

Open Access Open Access  Restricted Access Subscription Access

Framework Development for Detection of Skin Diseases Using Advanced Deep Learning Models and Suggestion of Pharmaceutical Remedy Products


Affiliations
1 Associate Professor, TAPMI School of Business, Manipal University Jaipur, 303 007, Rajasthan, India

   Subscribe/Renew Journal


Skin diseases are quite common in the present times and majority of the world population is facing these in some form or the other. However, inherent social stigma, attached to skin-related issues specially in the third world countries restricts people from seeking appropriate help from dermatologists unless the issues goes out of proportion. Several researches have been conducted on detection of skin related issues using Artificial Intelligence, however, no proper framework has been developed on how the use of these advanced technologies can directly help the affected individuals. The current work uses advanced models like CNN and Inception V3 models which perform better in terms of detecting cases of skin ailments from images and also suggest a framework through which proper information can be presented to the affected individuals and support them to take proper remedy by automatically suggesting one. This work is an effort to conglomerate the power of deep learning in the detection of skin related issues with the application of proper pharmaceutical products in the form of remedies for skin disease. This work unravels the path for practical application of deep learning algorithms for proper use of pharmaceutical products for benefits of patients.

Keywords

CNN, Deep Learning Application, Dermatology, Pharmaceutical Products, Skin Disease

Manuscript Received : August 3, 2022 ; Revised : August 17, 2022 ; Accepted : August 20, 2022. Date of Publication : October 5, 2022.

User
Subscription Login to verify subscription
Notifications
Font Size

  • V. B. Kumar, S. S. Kumar, and V. Saboo, "Dermatological disease detection using image processing and machine learning," in 2016 3rd Int. Conf. Artif. Intell. Pattern Recognit., 2016, pp. 16, doi: 10.1109/ICAIPR.2016.7585217.

  • M. Rajadurai, V. G. Vidhya, M. Ramya, and A. Bhaskar Ethno-medicinal plants used by the traditional healers of Pachamalai hills, Tamilnadu, India, Stud. EthnoMedicine, vol. 3, no. 1, pp. 3941, 2009.

  • E. Ayan, and H. M. ver, "Data augmentation importance for classification of skin lesions via deep learning," paper presented at 2018 Electric Electron., Comput. Sci., Biomed. Engineers' Meeting, 2018, pp. 14, doi: 10.1109/EBBT.2018.8391469.

  • Ali, A.-R., Li, J., Yang, G., and S. J. O'Shea, A machine learning approach to automatic detection of irregularity in skin lesion border using dermoscopic images, PeerJ Comput. Sci., vol. 6, e268, 2020, doi: 10.7717/peerj-cs.268.

  • D. A. Gavrilov, A. V., Melerzanov, N. N. Shchelkunov, and E. I. Zakirov, Use of neural network-based deep learning techniques for the diagnostics of skin diseases, Biomed. Eng., vol. 52, no. 5, pp. 348352, 2019, doi: 10.1007/s10527-019-09845-9.

  • A. S. Adamson and A. Smith, Machine learning and health care disparities in dermatology, JAMA Dermatology, vol. 154, no. 11, pp. 12471248, 2018, doi:10.1001/jamadermatol.2018.2348.

  • J. S. Alarifi, M. Goyal, A. K. Davison, D. Dancey, R. Khan, and M. H. Yap, Facial skin classification using Convolutional Neural Networks, in Karray, F., Campilho, A., Cheriet, F. (eds.) Image Anal. Recognit.ICIAR 2017. Lecture Notes in Comput. Sci.(), vol. 10317.Springer, Cham. doi: 10.1007/978-3-319-59876-5_53.

  • N. S. A. ALEnezi, A method of skin disease detection using image processing and machine learning, Procedia Comput. Sci, vol. 163, pp. 8592, 2019, doi: 10.1016/j.procs.2019.12.090.

  • Y. Li and L. Shen, Skin lesion analysis towards melanoma detection using deep learning network, Sensors, vol. 18, no. 2, p. 556, 2018, doi: 10.3390/s18020556.

  • N. Hameed, A. M. Shabut, M. K. Ghosh, and M. A. Hossain, Multi-class multi-level classification algorithm for skin lesions classification using machine learning techniques, Expert Syst. Appl., vol. 141, 112961, 2020, doi: 10.1016/j.eswa.2019.112961.

  • X. Dai, I. Spasi?, B. Meyer, S. Chapman, and F. Andres, Machine learning on mobile: An on-device inference app for skin cancer detection, In 2019 4th Int. Conf. Fog Mobile Edge Comput. (FMEC), pp. 301305, 2019. IEEE, doi: 10.1109/FMEC.2019.8795362.

  • S. Liaqat, K. Dashtipour, K. Arshad, and N. Ramzan, Non-invasive skin hydration level detection using Machine Learning, Electronics, vol. 9, no. 7, 1086, 2020, doi: 10.3390/electronics9071086.

  • H. R. Mhaske, and D. A. Phalke, "Melanoma skin cancer detection and classification based on supervised and unsupervised learning," 2013 Int. Conf. Circuits, C o n t ro l s C o m m u n . , 2 0 1 3 , p p . 1 5 , d o i : 10.1109/CCUBE.2013.6718539.

  • N. K. Mishra, and M. E. Celebi, An overview of melanoma detection in dermoscopy images using image processing and machine learning, 2016. [Online]. Available: arXiv preprint arXiv:1601.07843

  • Murugan, S. A. H. Nair, A. A. P. Preethi, and K. S. Kumar, Diagnosis of skin cancer using machine learning techniques, Microprocessors Microsystems, vol. 81, 103727, 2021.

  • Mobiny, A. Singh, and H. V. Nguyen, Risk-aware machine learning classifier for skin lesion diagnosis, J. Clin. Medicine, vol. 8, no. 8, p. 1241, 2019, doi: 10.3390/jcm8081241.

  • I. A. Ozkan, and M. Koklu, Skin lesion classification using machine learning algorithms, Int. J. Intell. Syst. Appl. Eng., vol. 5, no. 4, pp. 285289, 2017.

  • J. Rathod, V. Waghmode, A. Sodha, and P. Bhavathankar, Diagnosis of skin diseases using Convolutional Neural Networks, In 2018 2nd Int. Conf. Electronics, Commun. Aerosp. Technol., 2018, pp. 10481051, doi:10.1109/ICECA.2018.8474593.

  • A. Udrea, G. D. Mitra, D. Costea, E. C. Noels, M. Wakkee, D. M. Siegel, T. M. de Carvalho, , and T. E. C. Nijsten, Accuracy of a smartphone application for triage of skin lesions based on machine learning algorithms, J. Eur. Acad. Dermatology Venereology, vol. 34, no. 3, pp. 648655, 2020, doi: 10.1111/jdv.15935.

  • M. Vidya and M. V. Karki, "Skin Cancer Detection using Machine Learning Techniques," in 2020 IEEE Int. Conf. Electronics, Comput. Commun. Technol., 2020, pp. 15, doi: 10.1109/CONECCT50063.2020.9198489.

  • Vijayalakshmi, M. M. Melanoma skin cancer detection using image processing and machine learning, Int. J. Trend Sci. Res. Develop. vol. 3, no. 4, pp. 780784, 2019. doi: 10.31142/ijtsrd23936


Abstract Views: 157

PDF Views: 0




  • Framework Development for Detection of Skin Diseases Using Advanced Deep Learning Models and Suggestion of Pharmaceutical Remedy Products

Abstract Views: 157  |  PDF Views: 0

Authors

Subhabaha Pal
Associate Professor, TAPMI School of Business, Manipal University Jaipur, 303 007, Rajasthan, India

Abstract


Skin diseases are quite common in the present times and majority of the world population is facing these in some form or the other. However, inherent social stigma, attached to skin-related issues specially in the third world countries restricts people from seeking appropriate help from dermatologists unless the issues goes out of proportion. Several researches have been conducted on detection of skin related issues using Artificial Intelligence, however, no proper framework has been developed on how the use of these advanced technologies can directly help the affected individuals. The current work uses advanced models like CNN and Inception V3 models which perform better in terms of detecting cases of skin ailments from images and also suggest a framework through which proper information can be presented to the affected individuals and support them to take proper remedy by automatically suggesting one. This work is an effort to conglomerate the power of deep learning in the detection of skin related issues with the application of proper pharmaceutical products in the form of remedies for skin disease. This work unravels the path for practical application of deep learning algorithms for proper use of pharmaceutical products for benefits of patients.

Keywords


CNN, Deep Learning Application, Dermatology, Pharmaceutical Products, Skin Disease

Manuscript Received : August 3, 2022 ; Revised : August 17, 2022 ; Accepted : August 20, 2022. Date of Publication : October 5, 2022.


References





DOI: https://doi.org/10.17010/ijcs%2F2022%2Fv7%2Fi5%2F172578