Deep Learning and MRI Biomarkers for Precise Lung Cancer Cell Detection and Diagnosis

Abstract

Aim This research work aimed to combine different AI methods to create a modular diagnosis system for lung cancer, including Convolutional Neural Network (CNN), K-Nearest Neighbors (KNN), VGG16, and Recurrent Neural Network (RNN) on MRI biomarkers. Models have then been evaluated and compared in their effectiveness in detecting cancer, using a meticulously selected dataset containing 2045 MRI images, with emphasis being put on documenting the benefits of the multimodal approach for attacking the complexities of the disease. Background Lung cancer remains the most common cause of cancer death in the world, partly because of the challenges in diagnosis and the late stage of presentation. Although Magnetic Resonance Imaging (MRI) has become a critical modality in the identification and staging of lung cancer, too often, its effectiveness is curtailed by the interpretative variance among radiologists. Recent advances in machine learning hold great promise for augmenting the analysis of MRI and perhaps even increasing diagnostic accuracy with the start of timely treatment. In this work, the integration of advanced machine learning models with MRI biomarkers to solve these problems has been investigated. Objective The purpose of the present paper was to assess the effectiveness of integrating various machine-learning models with MRI biomarkers for lung cancer diagnostics, such as CNN, KNN, VGG16, and RNN. The dataset involved 2,045 MRI images, and the performances of the models were investigated by comparing their performance metrics to determine the best configuration of interconnection while underpinning the necessity of this multimodal approach for accurate diagnoses and, consequently, better patient outcomes. Methods For this study, we used 2045 MRI images, with 70% for training and 30% for validation. We used four machine-learning models to work on the photos: CNN, KNN, VGG16, and RNN. Systematic performance measures were included in the study: accuracy, recall, precision, and F1 score. The confusion matrices of this study compared the diagnostic power of every model to comprehend the pragmatic use of the models in a real-world predictive capability. Results The scores for the model were found to be better with the convolutional neural network in terms of recall, accuracy in measures tested, precision, and F1. The rest of the models, KNN, VGG16, and RNN, performed decently but were slightly lower in performance than CNN. The in-depth analysis through confusion matrices thus established the predictive reliability of the models in revealing immense insight into the capability of identifying true positives and minimizing false negatives in enhancing the diagnostic accuracy of lung cancer detection. Conclusion The findings obtained have shown further support and great potential for integrating advanced machine learning models with MRI biomarkers to improve lung cancer diagnosis. The high performance of CNN, high sensitivity and specificity of the KNN model, and robustness of results obtained from VGG16 and RNN models have pointed to the potential feasibility of AI in the accurate detection of cancer. Our work has shown strong support for this multimodal diagnostic approach, which might impact future practice in oncology through the integration of AI to improve treatment strategies and patient outcomes in medical imaging.