Abstract
Nowadays, skin cancer became a common disease where every 3 in 100 people are affecting from skin cancer. Previously doctors used to detect the cancer efficiently, but nowadays they are unable to detect. So, there is a drastic demand for computer-based detection systems. Usually, computer detects skin cancer from dermoscopic images by using deep learning techniques. Many researchers used ML and DL techniques to detect skin cancer and to find accurate results. But they did not perform well for new images. In this paper, we added CNN model to predict the skin cancer along with that, we proposed a model called Meta Block which has metadata along with dermoscopy images of patients that includes all records of patients that helps in predicting the cancer more accurately. In this paper, we used two different datasets for skin cancer classification and we used 5 different models and compared the results with previous research results. We found that by comparing the accuracies are increased by 10% in ISIC 2019 Dataset and in HAM1000 Dataset it is increased by 15%.
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Devarakonda, N., Murthy, M.V.R., Reddy, R.R.C., Shree Harsha, P.B.L. (2024). Skin Cancer Detection with Metadata Using Deep Learning Strategies. In: Shetty, N.R., Prasad, N.H., Nagaraj, H.C. (eds) Advances in Communication and Applications . ERCICA 2023. Lecture Notes in Electrical Engineering, vol 1105. Springer, Singapore. https://doi.org/10.1007/978-981-99-7633-1_16
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