A Structured Implemenbtation of Intellegent Decision Making for Indetifying TH Elevel of Lung Cancer Using AI

Lung cancer continues to be a major cause of cancer-related death, which emphasises how crucial early identification is to higher survival rates. Because it is difficult for radiologists to identify lung nodules by hand, a novel computer-aided decision-support equipment for lung a nodule detection was created. Our system, which is built on a 3D a Deep Convolutional Neural (3DDCNN), is a useful tool to help radiologists make lung cancer diagnostic decisions. In order to use the three-dimensional data from CT scans, we automatically selected possible areas of interest using a multi-Region Research Network (mRPN) and median intensity projection. Our Computer-Aided Detection (CAD) system performed better than expected thanks to training and validation using datasets in LUNA16, ANODE09, and LIDC-IDR. With a low false positive rate and a sensitivity rating of 98.4%, generality of 92%, AUROC = 96%, and accuracy of 98.51%, our method performs very well. With a low number of false positives and an amazing 98.7% sensitivity, our Cloud-Based 3DDCNN outperformed previous systems once cloud computing was included. This highlights the potential elderly deep learning in conjunction with cloud computing to provide precise and effective lung nodule diagnosis using CT imaging, hence offering significant assistance to healthcare providers during the treatment of patients with lung cancer.