Detection Of Skin Cancer Using Neural Architecture Search with Model Quantization

Skin cancer is the fifth most common cancer across the globe. One in every three cancers detected is skin cancer. Every year, between 2 and 3 million non-melanoma skin cancers and 132,000 melanoma skin cancers occur globally each year. The worldwide number of cases of carcinoma is increasing by epidemic proportions. Catching cancer early often allows for more treatment options. It is crucial to know which type of cancer one has because it affects the treatment options and prognosis. Hence, our work is primarily motivated by the importance of early and efficient detection of skin cancer. In this paper, a novel methodology has been proposed by incorporating Neural Architecture Search and Model Quantization technique, where deep convolutional neural network is utilized to build a skin cancer classifier, which can be deployed on low compute devices. To the best of our knowledge this is the first instance of utilizing Neural Architecture Search in combination with Model Quantization for the purpose of Skin Cancer detection. The yielded results show that, from the model proposed through this methodology, the model size can be reduced without causing a significant change in the accuracy. Thus, making this type of a skin classifier a more effective tool for skin cancer detection allows it for the usage on devices with limited memory and computational capacity.