Comparing the performance of linear regression versus deep learning on detecting melanoma skin cancer using apple core ML

Herry Sujaini, Enriko Yudhistira Ramadhan, Haried Novriando


Melanoma is a type of deadly skin cancer. The survival rate of the patients can fall as low as 15.7% if the cancer cell has reached its final stage. Delayed treatment of melanoma can be attributed to its likeness to that of common nevus (moles). Two machine learning models were developed, each with a different approach and algorithm, to detect the presence of melanoma. Image classification is using the regression algorithm, and object detection is using deep learning. The two models are then compared, and the best model is determined according to the achieved metrics. The testing was conducted using 120 testing data and is made up of 60 positive data and 60 negative data. The testing result shows that object detection achieved 70% accuracy than image classification’s 68%. More importantly, linear regression’s 43% false-negative rate is noticeably high compared to convolutional neural network’s (CNN) 25%. A false-negative rate of 43% means almost half of sick patients tested using image classification will be diagnosed as healthy. This is dangerous as it can lead to delayed treatment and, ultimately, death. Thus it can be concluded that CNN is the best method in detecting the presence of melanoma.


Apple core ML; Computer vision; Convoluted neural network; Linear regression; Melanoma

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