BlockChain and Deep Learning with Dynamic Pattern Features for Lung Cancer Diagnosis

Abstract

Cancers in the respiratory tract grow out of control in lung carcinoma, a deadly disease. Because cancers have irregular shapes, it can be challenging to diagnose them and determine their sizes and forms from imaging studies. Furthermore, a serious issue with health image inquiry is large disparity. Artificial intelligence and blockchain are two cutting-edge advances in the healthcare industry. This paper introduces a Blockchain with a deep learning network for the early diagnosis of lung cancer in an effort to address these problems. Images from CT scans and CXRs were included in the LIDC-IDRI and NIH Chest X-ray collection. Initially, these images are pre-processed by Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance the image clarity and reduce the noise. Then the Honey Badger optimization Algorithm (HBA) is used to segment the lung region from the pre-processed image. Morphological segments of the lung region are used to generate dynamic patterns. Finally, these patterns are aggregated into the deep neural Spiking Convolutional Neural Network (SCNN), which is the global model for classifying the images into normal and abnormal cases. Based on the classification, the SCNN model achieves 98.64% accuracy from the LIDC-IDRI database and 98.9% on the NH Chest X-ray lung image dataset. The experiments indicate that the proposed approach results in lower energy consumption and faster inference times. Furthermore, the interpretability of the classification findings is improved by the intrinsic explainability of SCNNs, offering more profound understanding of the decision-making process. With these benefits, SCNNs are positioned as a reliable and effective technique for classifying lung images, providing a significant advancement over current methods.