Lung tumor cell classification with lightweight mobileNetV2 and attention-based SCAM enhanced faster R-CNN

Early and precise detection of lung tumor cell is paramount for providing adequate medication and increasing the survivability of the patients. To achieve this, the Enhanced Faster R-CNN with MobileNetV2 and SCAM framework is bestowed for improving the diagnostic accuracy of lung tumor cell classification. The U-Net architecture optimized by Stochastic Gradient Descent (SGD) is employed to carry out clinical image segmentation. The developed approach leverages the advantage of the lightweight design MobileNetV2 backbone network and the attention mechanism called Spatial and Channel Attention Module (SCAM) for improving the feature extraction as well as the feature representation and localization process of lung tumor cell. The proposed method integrated a MobileNetV2 backbone network due to its lightweight design for deriving valuable features of the input clinical images to reduce the complexity of the network architecture. Moreover, it also incorporates the attention module SCAM for the creation of spatially and channel wise informative features to enhance the lung tumor cell features representation and also its localization to concentrate on important locations. To assess the efficacy of the method, several high performance lung tumor cell classification techniques ECNN, Lung-Retina Net, CNN-SVM, CCDC-HNN, and MTL-MGAN, and datasets including Lung-PET-CT-Dx dataset, LIDC-IDRI dataset, and Chest CT-Scan images dataset are taken to carry out experimental evaluation. By conducting the comprehensive comparative analysis for different metrics with respect to different methods, the proposed method obtains the impressive performance rate with accuracy of 98.6%, specificity of 96.8%, sensitivity of 97.5%, and precision of 98.2%. Furthermore, the experimental outcomes also reveal that the proposed method reduces the complexity of the network and obtains improved diagnostic outcomes with available annotated data.