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{ "item_title" : "Artificial Intelligence for Early Oral Cancer Detection", "item_author" : [" Priyanka D", "Yashoda R", "Manjunath P. Puranik "], "item_description" : "Oral cancer is one of the most common malignancies in India, primarily associated with tobacco use, and is often diagnosed at an advanced stage, resulting in poor prognosis. Early detection is essential to improve survival rates and reduce treatment costs. Artificial intelligence (AI) is emerging as a powerful tool for early detection of oral cancer by analyzing clinical images, histopathology, and patient data. Machine learning and deep learning algorithms can identify precancerous and cancerous lesions with high accuracy. AI assists in reducing diagnostic delays, especially in resource-limited settings. It supports clinicians in risk assessment, screening, and referral decisions. Integrating AI into diagnostic workflows may contribute to improved patient outcomes and reduced disease burden.", "item_img_path" : "https://covers4.booksamillion.com/covers/bam/6/20/845/534/6208455340_b.jpg", "price_data" : { "retail_price" : "52.00", "online_price" : "52.00", "our_price" : "52.00", "club_price" : "52.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Artificial Intelligence for Early Oral Cancer Detection|Priyanka D

Artificial Intelligence for Early Oral Cancer Detection

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Overview

Oral cancer is one of the most common malignancies in India, primarily associated with tobacco use, and is often diagnosed at an advanced stage, resulting in poor prognosis. Early detection is essential to improve survival rates and reduce treatment costs. Artificial intelligence (AI) is emerging as a powerful tool for early detection of oral cancer by analyzing clinical images, histopathology, and patient data. Machine learning and deep learning algorithms can identify precancerous and cancerous lesions with high accuracy. AI assists in reducing diagnostic delays, especially in resource-limited settings. It supports clinicians in risk assessment, screening, and referral decisions. Integrating AI into diagnostic workflows may contribute to improved patient outcomes and reduced disease burden.

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Details

  • ISBN-13: 9786208455347
  • ISBN-10: 6208455340
  • Publisher: LAP Lambert Academic Publishing
  • Publish Date: August 2025
  • Dimensions: 9 x 6 x 0.12 inches
  • Shipping Weight: 0.18 pounds
  • Page Count: 52

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