Detection of lung cancer using Ten Convolutional Neural Network Models based modified co-learning technique in PET/CT image

Background: A proposed Lung Cancer Detection System (LCDS) faces a major issue due to low spatial resolution in Positron Emission Tomography (PET) and low contrast in Computed Tomography (CT). However, radiologists are unable to notice lung nodules during the beginning stage of lung cancer. Method: Such an issue has been resolved by creating a modified co-learning technique which will be based on ten Convolutional Neural Network (CNN) models (Alexnet, VGG16, VGG19, Squeezenet, Googlenet, Inceptionv3, Mobilenetv2, Densenet201, Resnet18, Xception). This technique encodes modality specific features and utilizes them to acquire a spatially varying fusion map. These fusion maps are multiplied using a modality feature map for an utilization of image analysis. Result: A proposed LCDS attained 90 % sensitivity, 99.25% accuracy and 2.4 false positives per scan. By utilizing modified co-learning technique, our proposed LCDS attained 94.165 % sensitivity, 99.40 % accuracy and 1.6 false positives per scan in PET/CT. Conclusion: Our proposed LCDS attained tremendously minimum false positive rate and it is a promising technique in support of cancerous recognition due to improved sensitivity and accuracy.