A cad system design based on HybridMultiscale convolutional Mantaray network for pneumonia diagnosis

Pneumonia is one of the diseases that people may encounter in any period of their lives. Recently, researches and developers all around the world are focussing on deep learning and image processing strategies to quicken the pneumonia diagnosis as those strategies are capable of processing numerous X-ray and computed tomography (CT) images. Clinicians need more time and appropriate experiences for making a diagnosis. Hence, a precise, reckless, and less expensive tool to detect pneumonia is necessary. Thus, this research focuses on classifying the pneumonia chest X-ray images by proposing a very efficient stacked approach to improve the image quality and hybridmultiscale convolutional mantaray feature extraction network model with high accuracy. The input dataset is restructured with the sake of a hybrid fuzzy colored and stacking approach. Then the deep feature extraction stage is processed with the aid of stacking dataset by hybrid multiscale feature extraction unit to extract multiple features. Also, the features and network size are diminished by the self-attention module (SAM) based convolutional neural network (CNN). In addition to this, the error in the proposed network model will get reduced with the aid of adaptivemantaray foraging optimization (AMRFO) approach. Finally, the support vector regression (SVR) is suggested to classify the presence of pneumonia. The proposed module has been compared with existing technique to prove the overall efficiency of the system. The huge collection of chest X-ray images from the kaggle dataset was emphasized to validate the proposed work. The experimental results reveal an outstanding performance of accuracy (97%), precision (95%) and f-score (96%) progressively.