Identification of Skin Lesions by Using Single-Step Multiframe Detector

Abstract

An artificial intelligence algorithm to detect mycosis fungoides (MF), psoriasis (PSO), and atopic dermatitis (AD) is demonstrated. Results showed that 10 s was consumed by the single shot multibox detector (SSD) model to analyze 292 test images, among which 273 images were correctly detected. Verification of ground truth samples of this research come from pathological tissue slices and OCT analysis. The SSD diagnosis accuracy rate was 93%. The sensitivity values of the SSD model in diagnosing the skin lesions according to the symptoms of PSO, AD, MF, and normal were 96%, 80%, 94%, and 95%, and the corresponding precision were 96%, 86%, 98%, and 90%. The highest sensitivity rate was found in MF probably because of the spread of cancer cells in the skin and relatively large lesions of MF. Many differences were found in the accuracy between AD and the other diseases. The collected AD images were all in the elbow or arm and other joints, the area with AD was small, and the features were not obvious. Hence, the proposed SSD could be used to identify the four diseases by using skin image detection, but the diagnosis of AD was relatively poor.

Real-Life Barriers to Diagnosis of Early Mycosis Fungoides: An International Expert Panel Discussion

“…However, these techniques are not yet commercially available; flow cytometry and TCR-gene clonality assessment are currently the only routinely available diagnostics for peripheral blood. Other tools include reflectance confocal microscopy, allowing non-invasive visualization of characteristic MF skin morphology [60,61], and artificial intelligence algorithms based on clinical images and histopathology [62,63]. However, their place in the diagnostic process for MF remains unclear.…”

Transfer Learning via Artificial Intelligence for Guiding Implant Placement in the Posterior Mandible: an in vitro Study

“…SSD is one of the best AI models in image identi cation and location, which consist of at least 16 layers 31,32 . It is an effective object detector for multiple targets recognition within just one stage 10 . Yao-Kuang et al 31 used a SSD system for diagnosing esophageal cancer, which showed good diagnostic performance and the accuracy can achieve 90%.…”

“…AI refers to a serious of technologies that allow computers and machines to imitate human intelligence 8 . It has come to play an important role in healthcare, including analyzing a diverse array of patient data and simulating human logic to perform some tasks [9][10][11][12] . AI iteratively learn the intrinsic statistics underlying the pairing data and algorithm, to make plan on unseen data 8,10 .…”

“…It has come to play an important role in healthcare, including analyzing a diverse array of patient data and simulating human logic to perform some tasks [9][10][11][12] . AI iteratively learn the intrinsic statistics underlying the pairing data and algorithm, to make plan on unseen data 8,10 . Recently, there has been a signi cantly increasing tendency in the number of AI studies in medicine, mostly for disease detection and classi cation purposes 8, 10, [13][14][15] .…”

Hyperspectral Imaging Combined with Artificial Intelligence in the Early Detection of Esophageal Cancer

“…However, by using hyperspectral imaging technology combined with artificial intelligence deep learning methods to perform spectral data for esophageal cancer will offer a faster and more accurate diagnosis. The hyperspectral images have nanometer-level spectral intervals and hence the amount of spectrum information that can be detected is much larger than that of multispectral images [ 5 , 6 , 7 , 8 , 9 , 10 , 11 ]. The image spectrum conversion is the use of an imaging spectrometer to obtain an image that has a wide wavelength measurement range, the image is divided into a multispectral image and a hyperspectral image according to the spectral resolution.…”