Classification of Different Cancer Types by Deep Convolutional Neural Networks

In this study, ten different types of cancer were classified with deep convolutional neural networks (DCNN). A total of 10,000 MRI (Magnetic Resonance Imaging) data were used for ten cancer patients, including 1000 MRI data for each cancer type. Although the images were reduced to 28x28 pixels, the DCNN model performed classification with an accuracy rate of 0.98 after 27 seconds and 15 epochs of training. The error rate in the last epoch in the study is also very close to zero. A highly successful classification has been achieved with the proposed DCNN model.

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