Melanoma skin cancer detection using deep learning and classical machine learning techniques: A hybrid approach

Jinen Daghrir , Lotfi Tlig , Moez Bouchouicha (1) , Mounir Sayadi

1 LIS - Laboratoire d'Informatique et des Systèmes (LIS) (Marseille, Toulon)

Melanoma is considered as one of the fatal cancer in the world, this form of skin cancer may spread to other parts of the body in case that it has not been diagnosed in an early stage. Thus, the medical field has known a great evolution with the use of automated diagnosis systems that can help doctors and even normal people to determine a certain kind of disease. In this matter, we introduce a hybrid method for melanoma skin cancer detection that can be used to examine any suspicious lesion. Our proposed system rely on the prediction of three different methods: A convolutional neural network and two classical machine learning classifiers trained with a set of features describing the borders, texture and the color of a skin lesion. These methods are then combined to improve their performances using majority voting. The experiments have shown that using the three methods together, gives the highest accuracy level.

Mots clés: Melanoma detection; deep learning; classical machine learning; data fusion; majority voting

Domaines: Sciences de l'ingénieur [physics]

Jinen Daghrir, Lotfi Tlig, Moez Bouchouicha, Mounir Sayadi. Melanoma skin cancer detection using deep learning and classical machine learning techniques: A hybrid approach. International Conference on Advanced Technologies for Signal and Image Processing, Sep 2020, Sfax, Tunisia. ⟨10.1109/ATSIP49331.2020.9231544⟩. ⟨hal-03172718⟩