Efficient AI Algorithm for Preliminary Diagnosis of Dermatological Manifestations

Abstract

In addition to reviewing the present scenario, this studyendeavors to predict and outline future trends in AI applications within dermatology[1]. By leveraging insights from the evolving technological landscape, this researchaimstoprovidevaluableguidanceforresearchers, healthcare practitioners, and technology developers. Ultimately, this studycontributes to the ongoing discourse on AI\'s disruptive potential in dermatology[2], steering future breakthroughs and fostering advancements that elevate patient care and outcomes. This study aims to forecast and define future trends in AI applications in dermatology in addition to assessing the current situation. The objective of this research is to offer significant recommendations to researchers, healthcare professionals, and technology developers by utilizing insights from the rapidlychanging technological ecosystem. In the end, this research adds to the current conversation about artificial intelligence\'s disruptive potential in dermatology by directingfuturediscoveriesandencouragingdevelopments that improve patient care and results.

Introduction

Artificial Intelligence (AI) aims to develop computing systems that mimic human intelligence through learning, reasoning, and perception. AI types include Artificial Superintelligence, General AI, and Narrow AI, each with distinct capabilities. Dermatology, focused on skin, hair, and nail conditions, benefits greatly from AI, particularly in image recognition to improve diagnosis accuracy and early detection of skin diseases ranging from benign to malignant.

Several studies demonstrate AI’s potential in dermatology, such as machine learning models classifying multiple skin conditions, deep learning for pathology diagnosis, and distributed AI for remote applications. However, challenges remain, including the need for diverse AI models that fairly represent different skin types, model transparency, validation across clinical settings, large and varied datasets, and ethical considerations like patient privacy.

This study proposes an AI-based diagnostic tool leveraging machine learning and deep learning, specifically utilizing models like Xception, to classify skin diseases from images. The dataset includes 1,657 images categorized into 7 skin disease types. Preprocessing involves image scaling and careful dataset splitting to enhance model robustness. The Xception model showed the highest accuracy, achieving near or above 97% in classifying diseases such as acne, carcinoma, eczema, keratosis, milia, and rosacea.

The study highlights AI’s role in accelerating dermatological diagnosis, reducing workload, and moving toward personalized medicine, while also emphasizing ongoing challenges in data diversity, interpretability, clinical validation, and ethical deployment.

Conclusion

The project\'s goal is to use the Xception framework to build an AI-Based Tool for Preliminary Diagnosis of Dermatological Manifestations. This application makes use ofXception,awell-knowndeeplearningarchitecturethathas garneredattentionforit soutstandingresultsinpictureclassificationapplications.Afterbeingtrainedonawiderangeofdermatologicalconditions,theXceptionmodel demonstratesremarkableprecisioninrecognizingand classifyingskindiseases.Itis noteworthy how accurate the AI-poweredtoolthatusesthe Xceptionmodelforpreliminary dermatological diagnostics is. The model exhibits excellent accuracyindifferentiatingbetween variousskinlesionssuch asmilia,carcinoma,rosacea,acne,keratosis,eczema,and keratosis ([insertaccuracypercentagehere]).Thetool\'s excellent accuracy contributes to its ability to help medical practitionersmakeinitialdiagnosis basedonvisualdata collected from skin pictures. In conclusion, this work marks a substantial development in the use ofAI in dermatological diagnoses.Throughadependableandefficientmethodof earlyskinconditionassessment, thetechnologyimproves patientcareandhealthcareoutcomesbyutilizingdeep learning and the Xception model.

References

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Copyright

Copyright © 2025 Sidharth S Nambiar, PavanKumar Naik , Vignesh K. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.