An Improved CNNBased Pneumoconiosis Diagnosis Method on Xray Chest Film. Pneumoconiosis is one of the most serious occupational diseases in China, which seriously endangers the health of most workers in dust environments. The diagnosis of pneumoconiosis is very complex and cumbersome, which relies mostly on doctorxe2x80x99s medical knowledge and clinical reading experiences of Xray chest film. Traditional image processing approach has helped doctors to reduce the misdiagnosis but with lower accuracy. An improved CNNbased pneumoconiosis diagnosis method on Xray chest films is proposed to predict pneumoconiosis disease. The CNN structure is decomposed from (5times 5) convolution kernel into two (3times 3) convolution kernels to optimize the execution. Compared with GoogLeNet, the proposed GoogLeNetCF achieves higher accuracy and gives a good result in the diagnosis of pneumoconiosis disease.

Optimized CNNbased Diagnosis System to Detect the Pneumonia from Chest Radiographs Pneumonia is a high mortality disease that kills 50, 000 people in the United States each year. Children under the age of 5 and older population over the age of 65 are susceptible to serious cases of pneumonia. The United States spend billions of dollars fighting pneumoniarelated infections every year. Early detection and intervention are crucial in treating pneumonia related infections. Since chest xray is one of the simplest and cheapest methods to diagnose pneumonia, we propose a deep learning algorithm based on convolutional neural networks to identify and classify pneumonia cases from these images. For all three models implemented, we obtained varying classification results and accuracy. Based on the results, we obtained better prediction with average accuracy of (68%) and average specificity of (69%) in contrast to the current stateoftheart accuracy that is (51%) using the Visual Geometry Group (VGG16 also called OxfordNet), which is a convolutional neural network architecture developed by the Visual Geometry Group of Oxford. By implementing more novel lung segmentation techniques, reducing over fitting, and adding more learning layers, the proposed model has the potential to predict at higher accuracy than human specialists and will help subsidies and reduce the cost of diagnosis across the globe.