Lung cancer classification model using convolutional neural network with feature ranking process

Lung cancer is the leading cause of cancer-related deaths worldwide, highlighting the importance of early detection to improve patient outcomes. The goal of this study is to create a computer-aided diagnosis (CAD) system that detects and classifies lung cancer based on medical images using a Convolutional Neural Network (CNN) and feature extraction techniques. The study employs the LIDC-IDRI dataset, a comprehensive collection of lung cancer-related medical images, to achieve this goal. Contrast Limited Adaptive Histogram Equalization (CLAHE) is used to improve contrast and detail while reducing noise. The Sobel operator is used to refine the segmentation process by enhancing edges and contours in binary images. Morphological operations such as dilation and filling are used to refine the segmented regions even further. Feature extraction is used to extract statistical data and texture characteristics from segmented regions. Mean and variance calculations reveal information about brightness and variability within regions, whereas co-occurrence matrices and Gray-Level Co-occurrence Matrix (GLCM) properties quantify texture features. The correlation between different regions is also evaluated to assess their relationships. Using the pre-processed and ranked features as inputs, a CNN model with five hidden layers is trained. To classify the segmented regions into cancerous and non-cancerous classes, the model learns patterns and relationships in the data. A confusion matrix is used to assess the accuracy, specificity, and sensitivity of the model's predictions, with an emphasis on correctly identifying lung cancer-affected regions. The results show promising results, with the proposed CAD system identifying lung cancer-affected regions with an accuracy of 99.4375%. The system also outperforms other existing methods with a specificity of 99.12% and a sensitivity of 99.26%. These findings highlight the developed system's potential as a valuable tool for early lung cancer detection, assisting doctors in making accurate diagnoses and improving patient outcomes.