International Journal of Image, Graphics and Signal Processing(IJIGSP)
ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)
Published By: MECS Press
IJIGSP Vol.14, No.3, Jun. 2022
Mobile-Based Skin Disease Diagnosis System Using Convolutional Neural Networks (CNN)
Full Text (PDF, 634KB), PP.47-57
This paper presents a design and development of an Artificial Intelligence (AI) based mobile application to detect the type of skin disease. Skin diseases are a serious hazard to everyone throughout the world. However, it is difficult to make accurate skin diseases diagnosis. In this work, Deep learning algorithms Convolution Neural Networks (CNN) is proposed to classify skin diseases on the HAM10000 dataset. An extensive review of research articles on object identification methods and a comparison of their relative qualities were given to find a method that would work well for detecting skin diseases. The CNN-based technique was recognized as the best method for identifying skin diseases. A mobile application, on the other hand, is built for quick and accurate action. By looking at an image of the afflicted area at the beginning of a skin illness, it assists patients and dermatologists in determining the kind of disease present. Its resilience in detecting the impacted region considerably faster with nearly 2x fewer computations than the standard MobileNet model results in low computing efforts. This study revealed that MobileNet with transfer learning yielding an accuracy of about 85% is the most suitable model for automatic skin disease identification. According to these findings, the suggested approach can assist general practitioners in quickly and accurately diagnosing skin diseases using the smart phone.
Cite This Paper
MWP Maduranga, Dilshan Nandasena, "Mobile-Based Skin Disease Diagnosis System Using Convolutional Neural Networks (CNN)", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.14, No.3, pp. 47-57, 2022.DOI: 10.5815/ijigsp.2022.03.05
Haenssle, Holger A, "Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists," Annals of Oncology, 2018.
Marchetti, Michael A, "Results of the 2016 International Skin Imaging Collab oration International Symposium on Biomedical Imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma the accuracy of computer algorithms to," Journal of the American Academy of Dermatology, pp. 270-277., 2018.
Tschandl, Philipp, "Expert-level diagnosis of nonpigmented skin cancer by combined convolutional neural networks, JAMA dermatology," 2019.
ViDIR Group, Tschandl, Rosendahl, Kittler, 2018. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific Data, Volume 5
M W P Maduranga and Ruvan Abeysekara. Supervised Machine Learning for RSSI based Indoor Localization in IoT Applications. International Journal of Computer Applications 183(3):26-32, May 2021
M.W.P Maduranga, Ruvan Abeysekera, "TreeLoc: An Ensemble Learning-based Approach for Range Based Indoor Localization", International Journal of Wireless and Microwave Technologies (IJWMT), Vol.11, No.5, pp. 18-25, 2021.DOI: 10.5815/ijwmt.2021.05.03R.
EL SALEH, S. BAKHSHI and A. NAIT-ALI, "Deep convolutional neural network for face skin diseases identification," 2019 Fifth International Conference on Advances in Biomedical Engineering (ICABME), 2019, pp. 1-4, doi: 10.1109/ICABME47164.2019.8940336.
The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions, Harvard Dataverse, 2018, doi:10.7910/DVN/DBW86T
Chatterjee, C.C, 2019. Basics of the Classic CNN. [Online] Available at: https://towardsdatascience.com/basics-of-the-classic-cnn-a3dce1225add [Accessed 02 06 2021].
Zakaria, N.K., Jailani, R., Tahir, N.M., "Application of ANN in Gait Features of Children for Gender Classification," Vols. Procedia Computer Science 76, 235–242. https://doi.org/10.1016/j.procs.2015.12.348.
M. Mehdy, P. Ng, E. Shair, N. Saleh and C. Gomes, "Artificial Neural Networks in Image Processing for Early Detection of Breast Cancer," Comput. Math. Methods Med. 2017, 2017, 2610628.
Rathod, J.; Waghmode, V.; Sodha, A.; Bhavathankar, P, Diagnosis of skin diseases using Convolutional Neural Networks. In Proceedings of the 2018 Second International Conference on Electronics, Communication, and Aerospace Technology (ICECA)," Coimbatore, India, 29–31 March 2018.
Rathod, J.; Waghmode, V.; Sodha, A.; Bhavathankar, P, 2018. Diagnosis of skin diseases using Convolutional Neural Networks. Coimbatore, India, Second International Conference on Electronics, Communication and Aerospace Technology (ICECA).
Saha, S, 2018. A Comprehensive Guide to Convolutional Neural Networks — the ELI5 way Towards Data Science. [Online] Available at: https://towardsdatascience.com/a-comprehensive-guide-to-convolutional neural-networks-the-eli5-way-3bd2b1164a53
N. S. A. ALEnezi, "A Method Of Skin Disease Detection Using Image Processing And," in 16th International Learning & Technology Conference, Makkah , Saudi Arabia, 2019.
M. Mehdy, P. Ng, E. Shair, N. Saleh and C. Gomes, 2017. Artificial Neural Networks in Image Processing for Early Detection of Breast Cancer, s.l.: Comput. Math. Methods Med.
Fredes, C.; Valenzuela, ANaranjo-Torres, J.; Mora, M.; Hernández-García, R.; Barrientos, R.J, "A Review of Convolutional Neural Network Applied to Fruit Image Processing.," 2020.
M.W.P Maduranga, Ruvan Abeysekera, " Bluetooth Low Energy (BLE) and Feed Forward Neural Network (FFNN) Based Indoor Positioning for Location-based IoT Applications", International Journal of Wireless and Microwave Technologies(IJWMT), Vol.12, No.2, pp. 33-39, 2022
Rupak Bhakta, A. B. M. Aowlad Hossain, " Lung Tumor Segmentation and Staging from CT Images Using Fast and Robust Fuzzy C-Means Clustering", International Journal of Image, Graphics and Signal Processing (IJIGSP), Vol.12, No.1, pp. 38-45,2020.DOI: 10.5815/ijigsp.2020.01.05.
Wei, L., Gan, Q., Ji, T, 2018. Skin Disease Recognition Method Based on Image Colour and Texture Features. s.l.Computational and Mathematical Methods in Medicine.
M. M. I. Rahi, F. T. Khan, M. T. Mahtab, A. K. M. Amanat Ullah, M. G. R. Alam and M. A. Alam, "Detection Of Skin Cancer Using Deep Neural Networks," 2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), 2019, pp. 1-7, doi: 10.1109/CSDE48274.2019.9162400.
A. D. Andronescu, D. I. Nastac and G. S. Tiplica, "Skin Anomaly Detection Using Classification Algorithms," 2019 IEEE 25th International Symposium for Design and Technology in Electronic Packaging (SIITME), 2019, pp. 299-303, doi: 10.1109/SIITME47687.2019.8990764.