Application of Fuzzy Convolutional Neural Network for Disease Diagnosis: A Case of Covid-19 Diagnosis Through CT Scanned Lung Images

The medical diagnosis can be enhanced by intelligent and automatic diagnosis through advances in Information and Communication Technologies (ICT) during the Covid-19 pandemic. This chapter discusses the current scenario, fundamental concepts, and existing solutions for diagnosing corona based diseases and their limitations. The chapter presents a generic and hybrid intelligent architecture for disease diagnosis. The architecture considers CT scanned images along with other fuzzy parameters and classifies the images into various disease categories using a convolutional neural network. The fuzzy convolutional neural network has experimented on 100 CT scanned images of lungs with additional fuzzy symptoms to prove the architecture's utility. The working of the convolutional layer, pooling layer, fully connected layer, fuzzy membership functions, and training data sets used in the experiment are discussed in detail in this chapter. The results are analyzed and presented graphically with improvement in accuracy, sensitivity, and precision. The chapter concludes with applications of the architecture for other disease diagnoses using radiology images and also discusses limitations and future work enhancement.

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