Skin Diseases Detection Using Convolution Neural Network

Skin Diseases Detection Using Convolution Neural Network

Rishitha P1, Thippeeramma T2, Vaishnavi M R3, Bhoomika V4, Samara Mubeen5,

Arnav Praveen6

1Rishitha P (Information Science and Engineering, JNNCE)

2Thippeeramma T (Information Science and Engineering, JNNCE)

3Vaishnavi M R (Information Science and Engineering, JNNCE)

4Bhoomika V (Information Science and Engineering, JNNCE)

5Samara Mubeen (Information Science and Engineering, JNNCE)

6Arnav Praveen (Oakwood school 105 John Wilson Way, Morgan Hill, CA 95037 USA)

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Abstract - As long as dermatological care encounters challenges, advancements in the accuracy of diagnosis and the efficacy of treatment are imperative. Pioneering work has shown that integrating You Look Only Once (YOLO) architecture with Convolution Neural Networks (CNNs) is a promising alternative. This study investigates the transformative potential and provides fresh perspectives on the diagnosis of skin conditions. By means of technical investigation, we uncover its ability to transform still images into proactive changes in dermatological diagnostics. Furthermore, its wide-ranging implications are explored, encompassing enhancements to diagnosis precision, optimization of treatment strategies, and ultimately, better patient results. The paper emphasizes the crucial roles that CNNs and YOLO architecture will play in defining the path by closely reviewing empirical data and real-world applications.

Key Words: Convolution Neural Networks (CNN), You Look Only Once (YOLO) architecture, Image processing, Skin diseases classification, Diagnostic accuracy.