Abstract
Skin is considered to be the largest organ of the human body. By a numerous number of recent studies, dermatologists of renowned schools of medicine as well as American Academy of Dermatology have discovered that being the largest external organ, skin could be considered as a window in one’s health and through which signs of internal diseases could be discerned way before they are developed. Presently, various kinds of diseases are well known for causing high death rates, such as several heart diseases and strokes, persistent irritant lung disease, and respiratory disorders, and predominantly, diverse cancers are the widespread dominant cause of death worldwide. Early detection of any disease could increase the chances of wellness. Despite being a great phenomenon in modern health science, no remarkable work has been executed on this so far. From that point, in this paper, a universal methodology of detecting abnormalities on skin from images which is not visible by human eye compared with normal skin images applying a convolutional neural network (CNN) is proposed. We have collected 100 skin images from different people by standard camera, and by using various image augmentation techniques, an increased number of datasets are obtained. We have achieved a 70% of accuracy rate. We aim to attain a higher accuracy rate in future by enhancing the dataset and implement our model for small devices.
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Mehera, P., Mridha, M.F., Begum, N., Mohaiminul Islam, M. (2020). Internal Abnormalities’ Detection of Human Body Analyzing Skin Images Using Convolutional Neural Network. In: Uddin, M.S., Bansal, J.C. (eds) Proceedings of International Joint Conference on Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-3607-6_49
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DOI: https://doi.org/10.1007/978-981-15-3607-6_49
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