Accurate and Rapid Diagnosis of COVID-19 Pneumonia with Batch Effect Removal of Chest CT-Scans and Interpretable Artificial Intelligence

Since late 2019, COVID-19 has been spreading over the world and caused the death of many people. The high transmission rate of the virus demands the rapid identification of infected patients to reduce the spread of the disease. The current gold-standard test, Reverse-Transcription Polymerase Chain Reaction (RT-PCR), suffers from a high rate of false negatives. Diagnosis from CT-scan images as an alternative with higher accuracy and sensitivity has the challenge of distinguishing COVID-19 from other lung diseases which demand expert radiologists. In peak times, artificial intelligence (AI) based diagnostic systems can help radiologists to accelerate the process of diagnosis, increase the accuracy, and understand the severity of the disease. We designed an interpretable deep neural network to distinguish healthy people, patients with COVID-19, and patients with other lung diseases from chest CT-scan images. Our model also detects the infected areas of the lung and is able to calculate the percentage of the infected volume. We preprocessed the images to eliminate the batch effect related to CT-scan devices and medical centers and then adopted a weakly supervised method to train the model without having any label for infected parts and any tags for the slices of the CT-scan images that had signs of disease. We trained and evaluated the model on a large dataset of 3359 CT-scan images from 6 medical centers. The model reached a sensitivity of 97.75% and a specificity of 87% in separating healthy people from the diseased and a sensitivity of 98.15% and a specificity of 81.03% in distinguishing COVID-19 from other diseases. The model also reached similar metrics in 1435 samples from 6 unseen medical centers that prove its generalizability. The performance of the model on a large diverse dataset, its generalizability, and interpretability makes it suitable to be used as a diagnostic system.

Hyper-Heuristic Algorithm for Finding Efficient Features in Diagnose of Lung Cancer Disease

Background: Lung cancer was known as primary cancers and the survival rate of cancer is about 15%. Early detection of lung cancer is the leading factor in survival rate. All symptoms (features) of lung cancer do not appear until the cancer spreads to other areas. It needs an accurate early detection of lung cancer, for increasing the survival rate. For accurate detection, it need characterizes efficient features and delete redundancy features among all features. Feature selection is the problem of selecting informative features among all features. Materials and Methods: Lung cancer database consist of 32 patient records with 57 features. This database collected by Hong and Youngand indexed in the University of California Irvine repository. Experimental contents include the extracted from the clinical data and X-ray data, etc. The data described 3 types of pathological lung cancers and all features are taking an integer value 0-3. In our study, new method is proposed for identify efficient features of lung cancer. It is based on Hyper-Heuristic. Results: We obtained an accuracy of 80.63% using reduced 11 feature set. The proposed method compare to the accuracy of 5 machine learning feature selections. The accuracy of these 5 methods are 60.94, 57.81, 68.75, 60.94 and 68.75. Conclusions: The proposed method has better performance with the highest level of accuracy. Therefore, the proposed model is recommended for identifying an efficient symptom of Disease. These finding are very important in health research, particularly in allocation of medical resources for patients who predicted as high-risks