Generative adversarial network based data augmentation for CNN based detection of Covid-19

Towards classification and comprehensive analysis of AI-based COVID-19 diagnostic techniques: A survey

The unpredictable pandemic came to light at the end of December 2019, known as the novel coronavirus, also termed COVID-19, identified by the World Health Organization (WHO). The virus first originated in Wuhan (China) and rapidly affected most of the world's population. This outbreak's impact is experienced worldwide because it causes high mortality risk, many cases, and economic falls. Around the globe, the total number of cases and deaths reported till November 12, 2022, were >600 million and 6.6 million, respectively. During the period of COVID-19, several diverse diagnostic techniques have been proposed. This work presents a systematic review of COVID-19 diagnostic techniques in response to such acts. Initially, these techniques are classified into different categories based on their working principle and detection modalities, i.e. chest X-ray imaging, cough sound or respiratory patterns, RT-PCR, antigen testing, and antibody testing. After that, a comparative analysis is performed to evaluate these techniques' efficacy which may help to determine an optimum solution for a particular scenario. The findings of the proposed work show that Artificial Intelligence plays a vital role in developing COVID-19 diagnostic techniques which support the healthcare system. The related work can be a footprint for all the researchers, available under a single umbrella. Additionally, all the techniques are long-lasting and can be used for future pandemics.

Explainable deep learning diagnostic system for prediction of lung disease from medical images

Around the globe, respiratory lung diseases pose a severe threat to human survival. Based on a central goal to reduce contiguous transmission from infected to healthy persons, several technologies have evolved for diagnosing lung pathologies. One of the emerging technologies is the utility of Artificial Intelligence (AI) based on computer vision for processing wide varieties of medical imaging but AI methods without explainability are often treated as a black box. Based on a view to demystifying the rationale influencing AI decisions, this paper designed and developed a novel low-cost explainable deep-learning diagnostic tool for predicting lung disease from medical images. For this, we investigated explainable deep learning (DL) models (conventional DL and vision transformers (ViTs)) for performing prediction of the existence of pneumonia, COVID19, or no-disease from both original and data augmentation (DA)-based medical images (from two chest X-ray datasets). The results show that our experimental consideration of the DA that combines the impact of cropping, rotation, and horizontal flipping (CROP+ROT+HF) for transforming input images and then passed as input to an Inception-V3 architecture yielded a performance that surpasses all the ViTs and other conventional DL approaches in most of the evaluated performance metrics. Overall, the results suggest that the utility of data augmentation schemes aided the DL methods to yield higher classification accuracies. Furthermore, we compared five different class activation mapping (CAM) algorithms (GradCAM, GradCAM++, EigenGradCAM, AblationCAM, and RandomCAM). The result shows that most of the examined CAM algorithms were effective in identifying the attention region containing the existence of pneumonia or COVID-19 from the medical images (chest X-rays). Our developed low-cost AI diagnostic tool (pilot system) can assist medical experts and radiographers in proffering early diagnosis of lung disease. For this, we selected five to seven deep learning models and the explainable algorithms were deployed on a novel web interface implemented via a Gradio framework.

AFEX-Net: Adaptive feature extraction convolutional neural network for classifying computerized tomography images

Purpose

Deep convolutional neural networks are favored methods that are widely used in medical image processing due to their demonstrated performance in this area. Recently, the emergence of new lung diseases, such as COVID-19, and the possibility of early detection of their symptoms from chest computerized tomography images has attracted many researchers to classify diseases by training deep convolutional neural networks on lung computerized tomography images. The trained networks are expected to distinguish between different lung indications in various diseases, especially at the early stages. The purpose of this study is to introduce and assess an efficient deep convolutional neural network, called AFEX-Net, that can classify different lung diseases from chest computerized tomography images.

Methods

We designed a lightweight convolutional neural network called AFEX-Net with adaptive feature extraction layers, adaptive pooling layers, and adaptive activation functions. We trained and tested AFEX-Net on a dataset of more than 10,000 chest computerized tomography slices from different lung diseases (CC dataset), using an effective pre-processing method to remove bias. We also applied AFEX-Net to the public COVID-CTset dataset to assess its generalizability. The study was mainly conducted based on data collected over approximately six months during the pandemic outbreak in Afzalipour Hospital, Iran, which is the largest hospital in Southeast Iran.

Results

AFEX-Net achieved high accuracy and fast training on both datasets, outperforming several state-of-the-art convolutional neural networks. It has an accuracy of 99.7 % and 98.8 % on the CC and COVID-CTset datasets, respectively, with a learning speed that is 3 times faster compared to similar methods due to its lightweight structure. AFEX-Net was able to extract distinguishing features and classify chest computerized tomography images, especially at the early stages of lung diseases.

Conclusion

The AFEX-Net is a high-performing convolutional neural network for classifying lung diseases from chest CT images. It is efficient, adaptable, and compatible with input data, making it a reliable tool for early detection and diagnosis of lung diseases.