Deep learning classifiers for computer-aided diagnosis of multiple lungs disease

Abstract

Background: Computer aided diagnosis has gained momentum in the recent past. The advances in deep learning and availability of huge volumes of data along with increased computational capabilities has reshaped the diagnosis and prognosis procedures. Objective: These methods are proven to be relatively less expensive and safer alternatives of the otherwise traditional approaches. This study is focused on efficient diagnosis of three very common diseases: lung cancer, pneumonia and Covid-19 using X-ray images. Methods: Three different deep learning models are designed and developed to perform 4-way classification. Inception V3, Convolutional Neural Networks (CNN) and Long Short Term Memory models (LSTM) are used as building blocks. The performance of these models is evaluated using three publicly available datasets, the first dataset contains images for Lung cancer, second contains images for Covid-19 and third dataset contains images for Pneumonia and normal subjects. Combining three datasets creates a class imbalance problem which is resolved using pre-processing and data augmentation techniques. After data augmentation 1386 subjects are randomly chosen for each class. Results: It is observed that CNN when combined with LSTM (CNN-LSTM) produces significantly improved results (accuracy of 94.5 %) which is better than CNN and InceptionV3-LSTM. 3,5, and 10 fold cross validation is performed to verify all results calculated using three different classifiers Conclusions: This research concludes that a single computer-aided diagnosis system can be developed for diagnosing multiple diseases.