Skin Disease Prediction Using Deep Learning
DOI:
https://doi.org/10.48001/978-81-966500-2-5-8Keywords:
Deep Learning, Pattern Recognition, Image Processing, Skin Disease ClassificationAbstract
Diagnosing skin diseases can be a tricky business, requiring a lot of expertise and time. But what if we told you there's a way to make it faster and more accurate? Our research explores the use of deep learning to predict skin diseases, and the results are promising. We trained a special type of computer program called a convolutional neural network (CNN) to look at pictures of skin lesions and figure out what's going on. By feeding it a huge dataset of images, the CNN learned to recognize patterns and features that distinguish one skin condition from another. To make it even better, we used some clever tricks like transfer learning and data augmentation to fine-tune the model. By the use of attention mechanism and multidimensional fusion model became more efficient. This means that doctors could soon have a powerful tool to help them diagnose skin conditions quickly and accurately. Our research shows that deep learning has the potential to revolutionize the way we diagnose skin diseases. With this technology, doctors can make more accurate diagnoses, and patients can get the treatment they need sooner.
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Anitha, P., Krithka, G., & Choudhry, M. D. (2014). Machine Learning Techniques for learning features of any kind of data : A Case Study. International Journal of Advanced Research in Computer Engineering Technology, 3(12), 4324–4331.
Bateni, S. B., Nguyen, P., Eskander, A., Seung, S. J., Mittmann, N., Jalink, M., Gupta, A., Chan, K. K., Look Hong, N. J., & Hanna, T. P. (2023). Changes in Health Care Costs, Survival, and Time Toxicity in the Era of Immunotherapy and Targeted Systemic Therapy for Melanoma. JAMA Dermatology, 159(11), 1195–1204. https://doi.org/10.1001/jamadermatol.2023.3179
Carniciu, S., Hafi, B., Gkini, M.-A., Tzellos, T., Jafferany, M., & Stamu‐O’Brien, C. (2023). Secondary psychiatric disorders and the skin. Dermatological Reviews, 4(4), 162171. https://doi.org/10.1002/der2.211
Doi, K. (2007). Computer-aided diagnosis in medical imaging: Historical review, current status and future potential. Computerized Medical Imaging and Graphics, 31(4-5), 198–211. https://doi.org/10.1016/j.compmedimag.2007.02.002
Gautam, S., & Mittal, P. (2022). Systematic Analysis of Predictive Modeling Methods in Stock Markets. International Research Journal of Computer Science, 9(11), 377-385. https://doi.org/10.26562/irjcs.2022.v0911.01
Jora, R. B., Sodhi, K. K., Mittal, P., & Saxena, P. (2022). Role of Artificial Intelligence (AI) In meeting Diversity, Equality and Inclusion (DEI) Goals. 8th International Conference on Advanced Computing and Communication Systems, ICACCS 2022, 1687–1690. https://doi.org/10.1109/ICACCS54159.2022.9785266
Salvi, M., Loh, H. W., Seoni, S., Barua, P. D., García, S., Molinari, F., & Acharya, U. R. (2024). Multi-modality approaches for medical support systems: A systematic review of the last decade. Information Fusion, 103. https://doi.org/10.1016/j.inffus.2023.102134
Shamsul Arifin, M., Golam Kibria, M., Firoze, A., Ashraful Amini, M., & Yan, H. (2012). Dermatological disease diagnosis using color-skin images. Proceedings- International Conference on Machine Learning and Cybernetics, 5, 1675–1680. https: //doi.org/10.1109/ICMLC.2012.6359626
Yasir, R., Rahman, M. A., & Ahmed, N. (2015). Dermatological disease detection using image processing and artificial neural network. 8th International Conference on Electrical and Computer Engineering: Advancing Technology for a Better Tomorrow, ICECE 2014, 687–690. https://doi.org/10.1109/ICECE.2014.7026918
Zhang, Y., Han, J., Liu, J., Zhou, T., Suni, J., & Luo, J. (2017). Safety prediction of rail transit system based on deep learning. Proceedings- 16th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2017, 851–856. https://doi.org/10.1109/ICIS.2017.7960111
