With the fast evolution of medical imaging study, a great interest in skin cancer detection has been investigated with numerous computer algorithms. Generally, skin lesions are examined with a limited quantity of ground truth labeling. The most important part of the medical image’s detection is calculating the localization function which is normally evaluated on the Intersection over Union threshold (IoU). It helps to locate the lesion accurately to collect dominant features of the skin lesion. In this work, an object localization for skin lesion detection has been proposed using SSD- Mobilenet model on ISIC 2018 as a training and testing datasets. To evaluate the detection performance, the detection process has been achieved using two different methods; a real-time mobile application of Android camera (Galaxy S6), and Jupyter Notebook of TensorFlow Object Detection Application Program Interface (API). The total confidence scores detection quality (total mAP) is 96.04% with a total loss of 0.78. Experimental results reach 99% of detection accuracy when using Jupyter Notebook, while it reaches 100% with Android detection. The experiments have been executed on Ubuntu 16.04LTS GTX1070 @ 2.80GHZ x8 system.