Speculation Of Lung Cancer Using Machine Learning Approach

Timely identification of lung cancer can save the life of a patient. However, lung cancer is often not identified until it has reached advanced stages, where it may have already spread beyond the lungs, complicating treatment efforts. Due to its high mortality rate, lung cancer remains significant global health concern, making early detection crucial for effective treatment and improved patient survival rates. Tissue biopsy is frequently required to confirm a lung cancer diagnosis and to determine its type and stage. Computed tomography (CT) scans are commonly used in lung cancer diagnosis because of their high resolution and sensitivity in detecting lung nodules. Recent advancements in machine learning, particularly in neural networks like Convolutional Neural Networks (CNNs) is useful to increase the accuracy as well as efficiency of diagnosing and classifying lung nodules in CT scans. This article reviews and analyzes recent techniques in cancer diagnosis using machine learning, w focusing on CNNs for CT scan images. It covers methods using CNNs, including data preparation, model parameters, training strategies, and performance evaluation.