  Rapid growth in the development of medical imaging analysis technology has
been propelled by the great interest in improving computer-aided diagnosis and
detection (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 for
developing segmentation and ROI detection of lesions, as expert annotations are
laborious and expensive. Detecting the ROI is vital to locate lesions
accurately. In this paper, we propose the use of two deep object detection
meta-architectures (Faster R-CNN Inception-V2 and SSD Inception-V2) to develop
robust ROI detection of skin lesions in dermoscopic datasets (2017 ISIC
Challenge, PH2, and HAM10000), and compared the performance with
state-of-the-art segmentation algorithm (DeeplabV3+). To further demonstrate
the potential of our work, we built a smartphone application for real-time
automated detection of skin lesions based on this methodology. In addition, we
developed an automated natural data-augmentation method from ROI detection to
produce augmented copies of dermoscopic images, as a pre-processing step in the
segmentation of skin lesions to further improve the performance of the current
state-of-the-art deep learning algorithm. Our proposed ROI detection has the
potential to more appropriately streamline dermatology referrals and reduce
unnecessary biopsies in the diagnosis of skin cancer.
