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Smart Jaundice Diagnostic System

Smart Jaundice Diagnostic System

Abstract

Jaundice, a common condition in infants that results from high bilirubin levels in the blood, often requires early diagnosis and monitoring to prevent severe complications, especially in newborns. Traditional diagnostic methods can be time-consuming and subject to human error. This study proposes an approach for real-time jaundice detection using advanced image processing techniques and machine learning algorithms. By analyzing images captured in RGB color spaces, pixel values are extracted and processed through Otsu’s thresholding and morphological operations to detect color patterns indicative of jaundice. A classifier model is then trained to distinguish between normal and jaundiced conditions, offering an automated, accurate, and efficient diagnostic tool. The system’s potential to operate in real-time makes it particularly suited for clinical settings, providing healthcare professionals with timely insights to improve patient outcomes. The proposed method represents a significant innovation in healthcare, combining artificial intelligence and medical imaging to enhance the early detection and management of jaundice, reducing reliance on manual interventions and improving overall healthcare delivery.

Objective

โรคดีซ่าน ซึ่งเป็นภาวะทางการแพทย์ทั่วไปที่มีลักษณะการเหลืองของผิวหนังและดวงตา มักบ่งบอกถึงความผิดปกติของตับหรือเลือดที่อยู่เบื้องหลัง การตรวจพบในระยะเริ่มต้นมีความสำคัญอย่างยิ่ง โดยเฉพาะในทารกแรกเกิด ที่หากไม่ได้รับการรักษาโรคดีซ่าน อาจนำไปสู่ภาวะแทรกซ้อนร้ายแรงได้ วิธีการวินิจฉัยแบบดั้งเดิมต้องอาศัยการตรวจสอบด้วยสายตาหรือการทดสอบในห้องปฏิบัติการ ซึ่งอาจใช้เวลานานและมีข้อผิดพลาดได้ ความก้าวหน้าล่าสุดในด้านการประมวลผลภาพและแมชชีนเลิร์นนิงเสนอความเป็นไปได้ใหม่ ๆ สำหรับการตรวจจับที่แม่นยำ มีประสิทธิภาพ และแบบเรียลไทม์มากขึ้น ด้วยการวิเคราะห์รูปแบบสีผิว ปัญญาประดิษฐ์ (AI) สามารถทำให้การวินิจฉัยเป็นไปโดยอัตโนมัติ ทำให้รวดเร็วขึ้นและลดการพึ่งพาการประเมินโดยมนุษย์

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