Towards the Performance Investigation of Automatic Melanoma Diagnosis Applications

Abstract

Melanoma is a type of skin cancer, one of the fatal diseases that appear as an abnormal growth of skin cells and the lesion part often looks like a mole on the skin. Early detection of melanoma from skin lesion by means of screening is an important step towards a reduction in mortality. For this purpose, numerous automatic melanoma diagnosis models based on image processing and machine learning techniques are available for computer-based applications (CBA) and smartphone-based applications (SBA). Since, the smartphones are available as most accessible and easiest methods with built-in camera option, SBA are preferred over CBA. In this paper, we explored the available literature and highlighted the challenges of SBA in terms of execution time due to the limited computing power of smartphones. To resolve this issue of storage of the smartphones, we proposed to develop an SBA that can seamlessly process the image data on the cloud instead of local hardware of the smartphone. Therefore, we designed a study to build a machine learning model of melanoma diagnosis to measure the time taken in preprocessing, segmentation, feature extraction, and classification on the cloud and compared the results with the processing time on the smartphone's local machine. The results showed there is a significant difference of p value <0.001 on the average processing time taken on both environments. As the processing on the cloud is more efficient. The findings of the proposed research will be helpful for the developers to decide the processing platform while developing smartphone applications for automatic melanoma diagnosis.