Improved skin lesions detection using color space and artificial intelligence techniques

Sengupta, Sudhriti; Mittal, Neetu; Modi, Megha

doi:10.1080/09546634.2019.1708239

Cited by 25 publications

(22 citation statements)

References 12 publications

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“…Moreover, recent studies indicate that optical imaging is improved through deep learning algorithms 27 – 33 . This article proposes a proof-of-concept simulation model and provides experimental verification to enhance imaging through diffuse media using multiple modes of vortex beams and convolution neural networks.…”

Section: Introductionmentioning

confidence: 99%

Imaging through diffuse media using multi-mode vortex beams and deep learning

Balasubramaniam

Biton

Arnon

2022

Sci Rep

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Optical imaging through diffuse media is a challenging issue and has attracted applications in many fields such as biomedical imaging, non-destructive testing, and computer-assisted surgery. However, light interaction with diffuse media leads to multiple scattering of the photons in the angular and spatial domain, severely degrading the image reconstruction process. In this article, a novel method to image through diffuse media using multiple modes of vortex beams and a new deep learning network named “LGDiffNet” is derived. A proof-of-concept numerical simulation is conducted using this method, and the results are experimentally verified. In this technique, the multiple modes of Gaussian and Laguerre-Gaussian beams illuminate the displayed digits dataset number, and the beams are then propagated through the diffuser before being captured on the beam profiler. Furthermore, we investigated whether imaging through diffuse media using multiple modes of vortex beams instead of Gaussian beams improves the imaging system's imaging capability and enhances the network's reconstruction ability. Our results show that illuminating the diffuser using vortex beams and employing the “LGDiffNet” network provides enhanced image reconstruction compared to existing modalities. An enhancement of ~ 1 dB, in terms of PSNR, is achieved using this method when a highly scattering diffuser of grit 220 and width 2 mm (7.11 times the mean free path) is used. No additional optimizations or reference beams were used in the imaging system, revealing the robustness of the “LGDiffNet” network and the adaptability of the imaging system for practical applications in medical imaging.

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Section: Introductionmentioning

confidence: 99%

Imaging through diffuse media using multi-mode vortex beams and deep learning

Balasubramaniam

Biton

Arnon

2022

Sci Rep

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“…The use of A.I‐based technologies is a rather recent milestone, mostly—and logically—first devoted to medical concerns, 24‐28 where classic imaging techniques (standardized photographs, videos, optical microscopic images, etc) are used. A few works, medical‐oriented, attempted to use smartphone's images (not selfies) as primary sources of A.I‐based analysis 29‐31 .…”

Section: Discussionmentioning

confidence: 99%

“…As it is well established that facial expression alters the appearance of some facial signs, 24‐29 1,023 women from the three ethnics (Table 3), of comparable age, were asked to take 15 selfie images in a row with their own smartphone‘s frontal camera (Resolution ≥5Megapixels). Hence, these images were taken under indoor conditions, by adopting different subtle variations from the most neutral expression, that is slight smile, slight pout or disapproval, etc at their own convenience.…”

Section: Methodsmentioning

confidence: 99%

Developing an Artificial Intelligence (A.I)‐based descriptor of facial appearance that fits with the assessments of makeup experts

Flament

Zhang²,

Zhi³

et al. 2021

Skin Research and Technology

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Objective To develop an A.I‐based automatic descriptor that detects and grades, from selfie pictures, 23 facial signs, hairs included, as a help to making‐up procedures. Material and Methods The selfie images taken in very different conditions by 3326 women and men were used to create (90% of dataset) and validate (10% of dataset) a new algorithm architecture to appraise and grade 23 different facial signs such as lips, nose, eye color, eyebrows, eyelashes, and hair color as defined by makeup artists. Each selfie image was annotated by 12 experts and defined references to train Artificial Intelligence (A.I)‐based algorithm. Results As some the 23 signs present a continuous or discontinuous feature, these were analyzed by two different statistical approaches. The results provided by the automatic descriptor system were not only in good agreement with the expert's assessments but were even found of a better precision and reproducibility. This automatic descriptor system has proven a good and robust accuracy despite the very variable conditions in the acquisition of selfie pictures. Conclusion Such automatic descriptor system seems providing a valuable help in making‐up procedures and may extend to other activities such as Skincare or Haircare. As such it should allow large investigations to better evaluate the consumers’ needs of esthetical improvements.

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“…However, they are known for false convergence or local minima/maxima with large computation time. Several times they are unable to solve restricted optimisation problems effectively with dynamic data sets [13] [14]. Yu M [15] in his study discussed about various factors which affect the efficiency of GA, such as large population size, inefficient fitness function and mismatched crossover technique.…”

Section: Related Workmentioning

confidence: 99%

Early and Precise Detection of Pancreatic Tumor by Hybrid Approach with Edge Detection and Artificial Intelligence Techniques

Dhruv¹,

Mittal²,

Modi³

2018

EAI Endorsed Transactions on Pervasive Health and Technology

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INTRODUCTION: Pancreatic cancer is highly lethal as it grows, spreads rapidly and difficult to diagnose at its early stages. It can be identified through scan images. The tumorous images obtained from imaging techniques suffer from the drawback of cryptic data due to presence of unwanted noise and poor contrast. OBJECTIVE: To reduce the risk of pancreatic cancer, its detection and diagnosis at an early stage becomes crucial. METHODS: The proposed work encompasses the processing of CT scans of pancreatic tumor using classical and artificial intelligence based optimized edge detection techniques for optimization and detection of tumor. RESULTS: The simulation results are highly encouraging as evident from the far improved visibility of resultant images with Particle Swarm Optimization. CONCLUSION: The output image with PSO shows the quality enhanced CT images which helps in accurate detection and diagnosis of the pancreatic tumor at an early stage providing an aid in medical imaging.

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Improved skin lesions detection using color space and artificial intelligence techniques

Cited by 25 publications

References 12 publications

Imaging through diffuse media using multi-mode vortex beams and deep learning

Imaging through diffuse media using multi-mode vortex beams and deep learning

Developing an Artificial Intelligence (A.I)‐based descriptor of facial appearance that fits with the assessments of makeup experts

Early and Precise Detection of Pancreatic Tumor by Hybrid Approach with Edge Detection and Artificial Intelligence Techniques

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