A hierarchical optimisation framework for pigmented lesion diagnosis

The study of training hyperparameters optimisation problems remains underexplored in skin lesion research.This is the first report of using hierarchical optimisation to improve computational effort in a four-dimensional search space for the problem.The authors explore training parameters selection in optimising the learning process of a model to differentiate pigmented lesions characteristics.In the authors'demonstration,pretrained GoogleNet is fine-tuned with a full training set by varying hyperparameters,namely epoch,mini-batch value,initial learning rate,and gradient threshold.The iterative search of the optimal global-local solution is by using the derivative-based method.The authors used non-parametric one-way ANOVA to test whether the classification accuracies differed for the variation in the training parameters.The authors identified the mini-batch size and initial learning rate as parameters that significantly influence the model's learning capability.The authors'results showed that a small fraction of combinations(5%)from constrained global search space,in contrarily to 82%at the local level,can converge with early stopping conditions.The mean(standard deviation,SD)validation accuracies increased from 78.4(4.44)%to 82.9(1.8)%using the authors'system.The fine-tuned model's performance measures evaluated on a testing dataset showed classification accuracy,precision,sensitivity,and specificity of 85.3%,75.6%,64.4%,and 97.2%,respectively.The authors'system achieves an overall better diagnosis performance than four state-of-the-art approaches via an improved search of parameters for a good adaptation of the model to the authors'dataset.The extended experiments also showed its superior performance consistency across different deep networks,where the overall classification accuracy increased by 5%with this technique.This approach reduces the risk of search being trapped in a suboptimal solution,and its use may be expanded to network architecture optimisation for enhanced diagnostic performance.

Retrospective Analysis of Outcomes with a Unique IPL System

Introduction: Intense Pulsed Light (IPL) technology is well accepted in the medical aesthetic field for the treatment of various skin lesions, including pigmented and vascular lesions. The light penetrates into the skin and is selectively absorbed by lesion chromophore. Absorbed energy is converted into heat, coagulating the lesion, which naturally fades following the treatment. The current article presents a retrospective efficacy evaluation of an IPL device with high peak power. Methods: Representative treatment results were collected from several clinics based on photographs taken at baseline and after treatments. Photos were evaluated and analyzed for aesthetic improvement of the various skin conditions in different facial and body areas. Results: Analysis included cases of pigmented and vascular lesions, textural lesions, and more specific conditions such as melasma and rosacea. The two evaluators’ scoring demonstrated improvement in all cases according to the Global Aesthetic Improvement Scale (GAIS) scale. Conclusion: The vast experience gathered from the market in treating various skin lesions supports the safety and efficacy of the investigated IPL device. The device’s particular specifications contribute to the successful results and ease of treatment for the practitioner and the patient.

Enhanced Diagnostic Precision:Deep Learning for Tumors Lesion Classification in Dermatology

Skin cancer is a highly frequent kind of cancer.Early identification of a phenomenon significantly improves outcomes and mitigates the risk of fatalities.Melanoma,basal,and squamous cell carcinomas are well-recognized cutaneous malignancies.Malignant We can differentiate Melanoma from non-pigmented carcinomas like basal and squamous cell carcinoma.The research on developing automated skin cancer detection systems has primarily focused on pigmented malignant type melanoma.The limited availability of datasets with a wide range of lesion categories has hindered in-depth exploration of non-pigmented malignant skin lesions.The present study investigates the feasibility of automated methods for detecting pigmented skin lesions with potential malignancy.To diagnose skin lesions,medical professionals employ a two-step approach.Before detecting malignant types with other deep learning(DL)models,a preliminary step involves using a DL model to identify the skin lesions as either pigmented or non-pigmented.The performance assessments accurately assessed four distinct DL models:Long short-term memory(LSTM),Visual Geometry Group(VGG19),Residual Blocks(ResNet50),and AlexNet.The LSTM model exhibited higher classification accuracy compared to the other models used.The accuracy of LSTM for pigmented and non-pigmented,pigmented tumours and benign classes,and melanomas and pigmented nevus classes was 0.9491,0.9531,and 0.949,respectively.Automated computerized skin cancer detection promises to enhance diagnostic efficiency and precision significantly.