ADVANCED IMAGING TECHNIQUES IN CT SCANS FOR EARLY LUNG CANCER DETECTION USING NEURAL NETWORKS: A STUDY OF PAKISTAN’S HEALTHCARE SECTOR

Due to multiple healthcare system limitations in Pakistani medicare, lung cancer continues to represent one of the biggest health threats of this era despite various image processing methods proving insufficient for accurate diagnosis. An unreliable clinical approach exists in early diagnosis because existing techniques yield excessively misleading positive and negative results. Conventional diagnosis tools mainly use two-dimensional imaging techniques that both set limitations on observing complete tumor dimensions and lead to misidentifying cancer characteristics. The need for advanced detection techniques exists because current methods require improvement to detect tumors precisely at an early stage. Early detection of lung cancer remains vital to achieve better treatment results along with improved survival rates because lung cancer affects a large number of people worldwide. The discovery of Computed Tomography (CT) scans through technological advances made them essential for performing early-stage lung cancer detection. The research evaluates the acceptance level along with the diagnostic effectiveness of state-of-the-art imaging methods in CT scans used to discover lung cancer at early stages in Pakistan's medical space. Late-stage diagnosis of lung cancer leads to minimal treatment success because of its low survival rates. The proposed research method enables the detection of cancer cells at an early stage. A 3D convolutional neural network applies its analysis to the LIDIC-IDRI dataset to enhance diagnostic precision. The created model reached an accuracy level of 98.22%. A 3D convolutional neural network (CNN) analysis model is introduced in this research to raise the precision of lung cancer diagnosis detections. An implemented 3D CNN model examines volumetric CT data during analysis to provide superior malignant spot detection when compared to traditional 2D imaging systems. This method makes diagnoses more reliable through the LIDC-IDRI dataset while minimizing the wrong classification of patients. Additionally, the study will compare the use of CT scan technology in Pakistan with other countries, offering insights into how different healthcare systems have integrated these technologies.