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A CNN architecture to Diagnosis of Dermatology Melanoma Skin Cancer using parallel convolution.

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DeMe-Net

A CNN architecture to Diagnosis of Dermatology Melanoma Skin Cancer using parallel convolution.

The DeMe Net architecture is achieves 98.33% accuracy on testing data and 99.85% on the traininge detaset.

We used the HAM10000 dataset from Kaggle, which was published by ISIC for the 2018 ML Challenge.

Screenshot-1

The DeMe net architecture used 3x3, 5x5, 7x7, and 11x11 parallel conv2d layar for distributed computing.

Screenshot-2

We have used accuracy, precision, recall and f1-score as performance metrics to test the performance of the model.

Screenshot-3 Screenshot-4

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A CNN architecture to Diagnosis of Dermatology Melanoma Skin Cancer using parallel convolution.

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