An Approach for Detecting and classification of Lung Cancer Using Deep Learning and Machine Learning

Lung cancer is one of the top causes of cancer-related fatalities worldwide, demanding early and accurate detection for better patient outcomes. This work addresses the merging of neural network (ML) along with deep learning (DL) approaches to increase lung cancer diagnosis and classification. Using sophisticated convolution neural networks (CNNs) for image analysis and standard machine learning (ML) models like random forest algorithms and supported vector machines (SVMs) for structured data, the suggested technique achieves excellent diagnostic accuracy, avoiding false positives and negatives. Enhanced CNNs give higher efficiency with precision of 93%, recall of 98.5%, and an accuracy of 97%. Key indicators include cumulative risk, age, and weariness, whereas demographic characteristics have a modest influence. Visualization methods such as feature significance charts and confusion matrices verify model dependability. This hybrid AI-driven approach indicates great promise in transforming lung cancer diagnoses, however problems including data privacy, computing needs, and validation in clinical trials remain and This work utilizes artificial intelligence (AI) (ML) in conjunction with deep learning (DL) technology to produce a more precise and automated approach for lung cancer diagnosis and categorization.