Scholarlink

One of the most prevalent and fatal malignancies in the world is lung cancer. A projected 238,340 persons in the USA were given a lung cancer diagnosis in 2023 (117,550 men and 120,790 women). In 2020, there were over 2.2 million new cases of lung cancer diagnosed worldwide. As of 2023, lung cancer is still the top cause of cancer-related mortality, with an estimated 1.8 million deaths globally. Due mostly to late-stage diagnoses, the five-year relative survival rate for lung cancer is around 26.6% worldwide, underscoring the difficulties associated with early identification and treatment. Various methods, such as machine learning algorithms, are used to gather images, identify pertinent traits, divide the impacted region, choose the best features, and categorize the result. In conclusion, technical factors such as efficiency and limits are compared, obstacles are discussed, and future directions for lung cancer diagnosis and treatment are outlined.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Advancements and Analysis of Machine Learning Techniques for Lung Cancer Detection

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Radhika Thale,
  • Pradnya Valvi,
  • Janvhi Mishra,
  • Pranita Bhor

摘要

One of the most prevalent and fatal malignancies in the world is lung cancer. A projected 238,340 persons in the USA were given a lung cancer diagnosis in 2023 (117,550 men and 120,790 women). In 2020, there were over 2.2 million new cases of lung cancer diagnosed worldwide. As of 2023, lung cancer is still the top cause of cancer-related mortality, with an estimated 1.8 million deaths globally. Due mostly to late-stage diagnoses, the five-year relative survival rate for lung cancer is around 26.6% worldwide, underscoring the difficulties associated with early identification and treatment. Various methods, such as machine learning algorithms, are used to gather images, identify pertinent traits, divide the impacted region, choose the best features, and categorize the result. In conclusion, technical factors such as efficiency and limits are compared, obstacles are discussed, and future directions for lung cancer diagnosis and treatment are outlined.