TY - JOUR
A2 - zhang, le
AU - Liu, Shuai
AU - Chen, Zheng
AU - Zhou, Huahui
AU - He, Kunlin
AU - Duan, Meiyu
AU - Zheng, Qichen
AU - Xiong, Pengcheng
AU - Huang, Lan
AU - Yu, Qiong
AU - Su, Guoxiong
AU - Zhou, Fengfeng
PY - 2021
DA - 2021/06/04
TI - DiaMole: Mole Detection and Segmentation Software for Mobile Phone Skin Images
SP - 6698176
VL - 2021
AB - Motivation. The worldwide incidence and mortality rates of melanoma are on the rise recently. Melanoma may develop from benign lesions like skin moles. Easy-to-use mole detection software will help find the malignant skin lesions at the early stage. Results. This study developed mole detection and segmentation software DiaMole using mobile phone images. DiaMole utilized multiple deep learning algorithms for the object detection problem and mole segmentation problem. An object detection algorithm generated a rectangle tightly surrounding a mole in the mobile phone image. Moreover, the segmentation algorithm detected the precise boundary of that mole. Three deep learning algorithms were evaluated for their object detection performance. The popular performance metric mean average precision (mAP) was used to evaluate the algorithms. Among the utilized algorithms, the Faster R-CNN could achieve the best mAP = 0.835, and the integrated algorithm could achieve the mAP = 0.4228. Although the integrated algorithm could not achieve the best mAP, it can avoid the missing of detecting the moles. A popular Unet model was utilized to find the precise mole boundary. Clinical users may annotate the detected moles based on their experiences. Conclusions. DiaMole is user-friendly software for researchers focusing on skin lesions. DiaMole may automatically detect and segment the moles from the mobile phone skin images. The users may also annotate each candidate mole according to their own experiences. The automatically calculated mole image masks and the annotations may be saved for further investigations.
SN - 2040-2295
UR - https://doi.org/10.1155/2021/6698176
DO - 10.1155/2021/6698176
JF - Journal of Healthcare Engineering
PB - Hindawi
KW -
ER -