A Review on Deep Learning Approaches For Early-Stage Lung Cancer Detection And Diagnosis

Due in large part to delayed diagnosis yet the lack of certain early symptoms, lung cancer continues to rank among the most common and fatal malignancies in the world. Although improving survival rates requires early-stage discovery, traditional diagnostic techniques like biopsy or manual CT image processing are intrusive, time-consuming, and prone to interpreting errors. Recent developments in artificial intelligence, especially deep learning, have demonstrated great promise in tackling these issues by making it possible to detect cancerous patterns automatically, accurately, and efficiently. Compared to more conventional machine learning techniques like Support Vector Machines (SVMs), Convolutional Neural Networks, have become the most popular way for extracting complicated information from medical images. Additionally, sophisticated techniques like attention mechanisms, transfer learning, and hybrid machine learning models have improved interpretability, decreased overfitting, and increased generalization. The benefits, drawbacks, and clinical prospects of the deep learning techniques currently used in lung cancer identification and treatment are methodically examined in this review. The study highlights how deep learning is revolutionizing medical picture processing with the goal of promoting early diagnosis, lower mortality, and better patient outcomes.