Published Paper Details:

One of the main reasons for cancer-related fatalities worldwide is lung cancer. Patient survival rates and treatment outcomes are greatly enhanced by early identification. The development of computer-based systems for precise and effective lung cancer detection has advanced significantly thanks to developments in artificial intelligence (AI) and deep learning techniques. This research suggests a deep learning-based AI method for the early diagnosis of lung cancer.Convolutional neural network (CNN) architecture is used by the system to automatically analyse medical images, such as computed tomography (CT) scans or chest X-rays, and spot potentially malignant areas. The deep learning algorithm can understand intricate patterns and features indicating malignant tumours because it was trained on a vast dataset of annotated lung cancer photos. The suggested approach includes a number of steps in the detection of lung cancer. The first step in pre-processing medical photographs is to improve their quality and remove noise. The CNN-based model then carries out feature extraction and classification to find questionable areas in the images. The results are then refined in a post-processing stage to produce a final prediction. Extensive tests are run on a wide range of lung cancer imaging datasets to determine the viability of the proposed approach. To evaluate the model's diagnostic accuracy, performance metrics like sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC-ROC) are generated. The outcomes show the promising potential of deep learning-based AI-based lung cancer diagnosis. The suggested method successfully detects lung cancer with high accuracy and sensitivity, assisting radiologists and clinicians in early identification and decision-making. Additionally, the system's automated nature makes efficient and reliable analysis possible, potentially lowering the workload placed on medical practitioners. Deep learning methods are used in an AI-based method for finding lung cancer. The suggested system exhibits the capacity to enhance early diagnosis and support improved patient outcomes.The proposed framework can be refined and incorporated into clinical practise as AI and deep learning technology continues to evolve, opening the door for more effective lung cancer detection and treatment methods.