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All Journal Fidelity : Jurnal Teknik Elektro
Reynatha Chrestella Amandara Pangsibidang
Universitas Pertahanan Republik Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Optimization Detection of Skin Cancer using Deep Learning Reynatha Chrestella Amandara Pangsibidang; Syahrul Tuba
Fidelity : Jurnal Teknik Elektro Vol 5 No 1 (2023): Edisi Januari 2023
Publisher : Universitas Nusa Putra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52005/fidelity.v5i1.139

Abstract

Skin cancer is caused by the formation of abnormal cells that can assault or spread to numerous body sections. The skin cancer symptoms may include a mole with varying size, shape, and color, irregular edges, multiple hues, and sometimes, itching or bleeding. Exposure to the sun's UV radiation is attributed to more than 90 percent of known occurrences of Skin Cancer. In order to categorize cancer as malignant or benign, this study outlines the development of a classification system for skin cancer using deep learning. This system would use TensorFlow and Keras. The technique is used to classify skin cancer using the images from the data set that have been collected. After deployment, it was determined that the created Convolutional 2-D layer system was 78% accurate.
Co-Authors Syahrul Tuba