Optimizing Cancer Detection: A CNN Approach for Lung and Colon Cancer

This paper focuses on a deep learning-based technique using CNNs for the identification of cancer from the lung and colon histology images. Therefore, through using a large dataset, we developed a very robust CNN model that is able to give right subtype annotations of colon and lung cancer, which we define as benign tissue, squamous cell carcinoma, and adenocarcinoma. The proposed model complements dense layers, pooling and multiple kin convolution layers for extracting and training the more detailed histopathology images. By the assessment of the measures such as accuracy, loss, and confusion matrices, we made sure that we conducted a proper assessment, as well as provided a proper distribution of training and validation data. These results demonstrate an improvement in the classification accuracy for the model and, more importantly, better capability to differentiate between benign and malignant tissues. The paper therefore shows how CNNs can be applied to automatically diagnose the cancer thus making it easier for pathologists to use the tool in the clinical practice. Therefore, our model, receiving high accuracy due to successful data augmentation techniques and changing of various hyperparameters, can be considered as a promising approach to practical medical image analysis. Thus, this finding states the direction to better detection methods in clinical practice by advancing the investigation in cancer diagnosis.