Deep Lungs: Revolutionizing Cancer Detection with Neural Networks
DOI:
https://doi.org/10.46610/JoAESP.2025.v02i02.004Keywords:
CT imaging, Deep learning, Lung cancer detection, MATLAB, Radiology assistanceAbstract
The increasing importance or concern of health-related risks due to rapidly changing environmental conditions, climate patterns, and lifestyle habits has raised significant concern in recent years. Among these, lung-related diseases have emerged as a leading cause of mortality in India, ranking second only to cardiovascular diseases. In 2022, globally, lung ailments accounted for approximately 2.48 million new cases and 1.8 million deaths, underscoring the urgency for timely detection and management. Early diagnosis remains critical in mitigating severe complications and reducing mortality rates associated with such conditions. Chest X-rays continue to be a primary and reliable tool for clinical diagnosis; however, the integration of advanced technologies, such as deep learning, with medical imaging like chest CT scans has demonstrated transformative potential. This paper proposes a deep learning-based diagnostic framework aimed at facilitating the accurate and timely identification of lung cancer from chest CT images. The system is designed to assist radiologists by providing high-accuracy predictions in real-time, thereby improving diagnostic capabilities, particularly in rural and underserved regions where access to expert radiological consultation is limited. A user-friendly Graphical user interface (GUI) developed using MATLAB enables seamless image loading and direct output generation, allowing for swift deployment in clinical and laboratory settings for real-time diagnosis.
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