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Rapid growth in the development of medical imaging analysis technology hasbeen propelled by the great interest in improving computer-aided diagnosis anddetection (CAD) systems for three popular image visualization tasks:classification, segmentation, and Region of Interest (ROI) detection. However,a limited number of datasets with ground truth annotations are available fordeveloping segmentation and ROI detection of lesions, as expert annotations arelaborious and expensive. Detecting the ROI is vital to locate lesionsaccurately. In this paper, we propose the use of two deep object detectionmeta-architectures (Faster R-CNN Inception-V2 and SSD Inception-V2) to developrobust ROI detection of skin lesions in dermoscopic datasets (2017 ISICChallenge, PH2, and HAM10000), and compared the performance withstate-of-the-art segmentation algorithm (DeeplabV3+). To further demonstratethe potential of our work, we built a smartphone application for real-timeautomated detection of skin lesions based on this methodology. In addition, wedeveloped an automated natural data-augmentation method from ROI detection toproduce augmented copies of dermoscopic images, as a pre-processing step in thesegmentation of skin lesions to further improve the performance of the currentstate-of-the-art deep learning algorithm. Our proposed ROI detection has thepotential to more appropriately streamline dermatology referrals and reduceunnecessary biopsies in the diagnosis of skin cancer.

Deep Learning Methods and Applications for Region of Interest Detection in Dermoscopic Images