LWSNet - a novel deep-learning architecture to segregate Covid-19 and pneumonia from x-ray imagery

Abstract

Automatic detection of lung diseases using AI-based tools became very much necessary to handle the huge number of cases occurring across the globe and support the doctors. This paper proposed a novel deep learning architecture named LWSNet (Light Weight Stacking Network) to separate Covid-19, cold pneumonia, and normal chest x-ray images. This framework is based on single, double, triple, and quadruple stack mechanisms to address the above-mentioned tri-class problem. In this framework, a truncated version of standard deep learning models and a lightweight CNN model was considered to conviniently deploy in resource-constraint devices. An evaluation was conducted on three publicly available datasets alongwith their combination. We received 97.28%, 96.50%, 97.41%, and 98.54% highest classification accuracies using quadruple stack. On further investigation, we found, using LWSNet, the average accuracy got improved from individual model to quadruple model by 2.31%, 2.55%, 2.88%, and 2.26% on four respective datasets.

A Novel Fusion Model of Hand-Crafted Features With Deep Convolutional Neural Networks for Classification of Several Chest Diseases Using X-Ray Images

“…All layers are directly connected, the feature map's size is preserved, identity mapping attributes are integrated organically, it offers both shallow and deep supervision, and it may recycle previously used features. Conversely, VGG models with a depth of 16-19 weight layers and very small size (3x3) convolutional kernel showed a significant improvement over the state-ofthe-art (SOTA) models in terms of classification accuracy and validation error [60]. Table 3 presents a detailed summary of the parameters used in implementing the DCNN models and Figure 9 illustrates the architecture of these models.…”

“…To begin, a difference-of-Gaussian function is applied to the complete CXR image to locate the areas of interest over the whole of the image. The subsequent step is known as "key point localization," and it involves the application of a precise model to determine the specific coordinates and size of each prospective key point [58][59][60]. By examining the stability metrics of the nodes, crucial nodes are identified.…”

“…By examining the stability metrics of the nodes, crucial nodes are identified. In the final step, directions of local CXR image gradients are employed to establish an orientation to each key point position [60]. In the fourth stage, we compute the local image gradients at the scale that you have selected at each critical point.…”

“…In this investigation, in addition to the softmax layer, we also made use of the quadratic support vector machine (Q-SVM) kernel [59], the AdaBoost [60], and the random forest (RF) [61]. These ML classifiers were utilized in a broad variety of investigations [57][58][59][60][61] as a result of their efficiency, ease, and affluence of execution in terms of health application.…”

Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review

“…Those frameworks are specially built for COVID-19 detection. With the help of CNN, researchers were able to find in-depth analyses of medical imaging modalities [ 170 ] using a variety of deep learning frameworks [ 171 ]. By analyzing biomedical and clinical data, healthcare experts and academics can learn about new ways to serve the patient community [ 21 ].…”