Enhancing Lung Cancer Diagnosis Through Convolutional Neural Networks: A Comprehensive Study on Image-Based Detection and Classification

This study investigates how well image-based detection and classification of lung cancer may be achieved with Convolutional Neural Networks (CNNs). The goal of the project is to apply deep learning techniques to improve the efficiency and accuracy of lung cancer diagnosis while addressing the shortcomings of current diagnostic methods. With the help of a well selected dataset of lung pictures, used a methodical approach that included data collecting, preprocessing, and the creation of a CNN model specifically designed for this purpose. The experimental results show that the suggested model performs better than previous methods, with a noticeable increase in accuracy, sensitivity, and specificity. Our results highlight CNNs' potential to transform the diagnosis of lung cancer by offering a thorough grasp of their capabilities for image-based detection and classification. The findings of this study have ramifications for the larger field of medical imaging and open the door to quicker and more precise lung cancer diagnosis, which will lead to better patient outcomes.