SKIN CANCER DETECTION USING DEEP LEARNING

SKIN CANCER DETECTION USING DEEP LEARNING

K LOKESHWAR1, MD ABUBAKER2, M ANITHA3

1,2 UG Scholar, Department of CSE, Kingston College, Vellore-59

3Asst.Professor, Department of CSE, Kingston College, Vellore-59

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Abstract – Cancer has been described as the most serious problem affecting public health because it causes so many deaths each year. Skin cancer, which arises in the uppermost layer of the skin, is one of the most prevalent types of cancer. Previously, various imaging modalities and protein sequences were utilized in conjunction with machine-learning methods to detect skin cancer. The disadvantage of the AI approaches is that they require human-designed highlights, which is an exceptionally relentless and time-taking action. By enabling automatic feature extraction, deep learning partially addressed this issue. Using the ISIC public dataset, convolution-based deep neural networks were used in this study to detect skin cancer. In visual imaging tasks, CNNs have provided the highest accuracy. The CNN order model will be created in Python utilizing Keras and Tensorflow in the backend. By using a variety of layers to train the network, including but not limited to Convolutional layers, Dropout layers, Pooling layers, and Dense layers, the model is developed and tested with various network architectures. For early convergence, the model will also employ Transfer Learning methods.

Key Words:  Neural networks, Skin cancer, Deep learning, Convolution neural network, CNN, Melanoma.