A capsule convolutional neural network method for treatment of skin lesions in COVID-19 disease detection based image classification

• Main Authors: Kulandaivel, Madhumitha, Manikantan, P. Karthik, Tripathi, Tuhina, Mehrotra, Manya
• Format: Conference Proceeding
• Language: English
• Subjects: Artificial neural networks; COVID-19; Datasets; Effectiveness; Image analysis; Image classification; Image enhancement; Machine learning; Medical imaging; Modules; Neural networks; Performance evaluation; Preprocessing
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/5.0217547

Capsule Conventional Networks are one of most used AI methods in the area of medical image analysis and categorization. Various modules utilize deep capsule convolutional networks to analyze selected chest X-ray images related to COVID-19. Due to the lack of sufficient availability of medical image datasets, applying dense and effective Capsule CNN is an arduous task. In order to overcome challenges such as limited image size and inadequate datasets, the dataset implements diverse preprocessing techniques across multiple stages. These methods are employed to ensure successful training of the dataset using capsule Convolutional Neural Networks and achieve optimal and accurate results. Additionally, transfer learning is incorporated to enhance the overall training process. The dataset undergoes various preprocessing stages, including dataset balancing, image analysis by medical advisors, and augmentation techniques for the images. This proposed module was evaluated in 2 varied situations. In the case, fine clinical images and scans were preprocessed and assessed and it achieved 100% accuracy was achieved. In the second case, it was an exclusive dataset and the module proficiency was 99%. The performance of the proposed model demonstrates strong efficacy when applied to a specific dataset for testing and evaluation.