CNN and Fuzzy logic based hybrid approach for lung cancer detection and report generation

Abstract

Lung cancer, also known as malignant tumours, is one of the deadly diseases that are prevalent worldwide. It is brought on by the lung tissues' hesitant cell growth. Only when the disease is in its early stages can treatment be begun to cure it. The results of blood tests and computed tomography (CT) scanning are used to find this. Emphasis on the tumours area allows for the detection of the aberrant picture. The sample, which is in jpg format, consists of CT scan images. Convolutional neural networks are used to train the developed framework (CNN). The benefit of using CNNs formed with fuzzy correlation maps is that they do not require considerable image thresholding or the scarce supply of specialized training data. The pre - trained models AlexNet CNN algorithm is presented the output of the fuzzy logic. A deep CNN requires a significant amount of data to analyse in order to get an accurate output. To handle the problem of the restricted size of the CNN set of data, a fuzzy logic-based fuzzy association map is proposed.This work analyses fuzzy-based CNN image classification models and proposes a hybrid CNN fuzzy logic.The use of present technology would increase as a result of this early cancer detection strategy, and cancer prevention would advance.