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In medical healthcare, enormous archives of clinical data about patients’ biographical information and condition specifics are kept. These archives are crucial for doctors as they quickly diagnose illnesses, making it easier to administer the proper medicines and save lives. Early diagnosis is of the utmost importance, especially when it comes to cancer, as it can significantly increase survival rates. On a global scale, lung cancer holds the unfortunate distinction of being the third most widespread type of cancer. Nevertheless, many lives are in danger due to the potential mistakes in the early detection of this disorder. The tissue that forms air passageways is the target of lung cancer as it begins and spreads there. It is commonly acknowledged that this particular form of cancer is accountable for a significant majority of cancer-related deaths worldwide. Annually, approximately 47 thousand individuals receive this diagnosis across the globe. This endeavor aims to present healthcare practitioners and radiologists with a distinctive approach that employs various deep-learning techniques. This approach seeks to enable the early identification of both malign and non-cancerous lung ailments, effectively tackling this concern. This work describes a practical, fully automated approach to The recognition and classification of lung cancer. The primary goal of this system is Timely diagnosis of cancer to either save lives or reduce mortality rates. Profound learning developments have significantly accelerated the field of medical imaging's expansion. This expansion includes numerous apps that use both text and medical images. Healthcare experts have found deep learning-powered technologies to be quite helpful in improving the precision and speed of detection and categorization of lung nodules. This work emphasizes on the most recent developments in using neural network learning approaches for imaging-based early Lung cancer detection. In summary, this study emphasizes the possibility of employing the proposed framework to bolster and augment doctors’ capacity in the precise diagnosis of lung cancer. The study emphasizes the usefulness, skill, and worth of this strategy in comparison to other methods that have been put into practice.

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A Comprehensive Review of Representation Learning Models for Pulmonary Nodule Extraction in Lung Cancer Diagnosis

  • 会议论文

  • 发表日期：

    2025年

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  • L. Sandhya,
  • Md. Sameeruddin Khan

摘要

In medical healthcare, enormous archives of clinical data about patients’ biographical information and condition specifics are kept. These archives are crucial for doctors as they quickly diagnose illnesses, making it easier to administer the proper medicines and save lives. Early diagnosis is of the utmost importance, especially when it comes to cancer, as it can significantly increase survival rates. On a global scale, lung cancer holds the unfortunate distinction of being the third most widespread type of cancer. Nevertheless, many lives are in danger due to the potential mistakes in the early detection of this disorder. The tissue that forms air passageways is the target of lung cancer as it begins and spreads there. It is commonly acknowledged that this particular form of cancer is accountable for a significant majority of cancer-related deaths worldwide. Annually, approximately 47 thousand individuals receive this diagnosis across the globe. This endeavor aims to present healthcare practitioners and radiologists with a distinctive approach that employs various deep-learning techniques. This approach seeks to enable the early identification of both malign and non-cancerous lung ailments, effectively tackling this concern. This work describes a practical, fully automated approach to The recognition and classification of lung cancer. The primary goal of this system is Timely diagnosis of cancer to either save lives or reduce mortality rates. Profound learning developments have significantly accelerated the field of medical imaging's expansion. This expansion includes numerous apps that use both text and medical images. Healthcare experts have found deep learning-powered technologies to be quite helpful in improving the precision and speed of detection and categorization of lung nodules. This work emphasizes on the most recent developments in using neural network learning approaches for imaging-based early Lung cancer detection. In summary, this study emphasizes the possibility of employing the proposed framework to bolster and augment doctors’ capacity in the precise diagnosis of lung cancer. The study emphasizes the usefulness, skill, and worth of this strategy in comparison to other methods that have been put into practice.