DiaMole: Mole Detection and Segmentation Software for Mobile Phone Skin Images

Abstract

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.

RETRACTED: Utilizing Generative Adversarial Networks for Acne Dataset Generation in Dermatology

“…Srinivasu et al [10], Wu et al [11], and Xiang et al [12] applied deep learning techniques for skin disease classification, utilizing neural networks like MobileNet V2, CNNs, and LSTM for improved diagnostic accuracy. Zhao et al [13] and Liu et al [14] focused on interpretable skin lesion classification using novel Convolutional Neural Network (CNN) algorithms and mole detection and segmentation software for mobile phone skin images.…”