Automated Subtype Classification of Lung Cancer through CNNs and Histopathological Images

Lung cancer is still a major worldwide health concern, and better patient outcomes depend on early identification. Using Convolutional Neural Networks (CNNs) on histopathology pictures, this study suggests a potential method for automated lung cancer identification. The methodology entails training the CNN model after preprocessing the dataset to include picture augmentation and normalization. Deep learning techniques are used to process histopathological images in order to extract discriminative features and categorize the images into different subtypes of lung cancer, such as adenocarcinoma, benign tissue, and squamous cell carcinoma. In addition to visual inspection of confusion matrices, performance evaluation metrics include recall, accuracy, precision, and F1-score. The performance of the model is also compared with the state-of-the-art techniques in order to assess its efficacy and potential for generalization. The results show how well the CNN-based approach can identify and classify lung cancer from histopathological images, underscoring its potential to assist pathologists in diagnosis and treatment planning.