Enhanced Lung Cancer Identification via Deep Learning-Powered Categorization and Segmentation

Since lung cancer is still one of the world's top causes of death, improving patient survival rates requires early identification and accurate categorization. More effective and dependable methods are required since traditional segmentation techniques frequently have inconsistencies because of observer variability and long processing durations. The segmentation and classification of lung tumors from CT scans has been effectively automated by recent developments in deep learning, such as CNNs, U-Net, and transformer-based architectures. The influence of cutting-edge deep learning models on improving detection sensitivity and specificity is examined in this work, along with the difficulties in integrating them clinically. These models decrease false-positive rates while increasing segmentation accuracy by utilizing attention modules, sophisticated feature extraction techniques, and encoder-decoder frameworks. Furthermore, to improve diagnostic accuracy and resilience, The suggested adaptive neuro-fuzzy inference system (ANFIS) opens the door for more automated and dependable lung cancer detection systems by fusing the advantages of fuzzy logic, neural networks, and machine learning.