Colon cancer is a type of cancer that affects the colon (large intestine) or rectum. It is one of the most common kinds of cancer worldwide and can cause severe harm and death. Early detection of colon cancer is especially difficult because of cancer cells overlap, making identification more difficult. Also, classifying cancerous cells in histopathology images is difficult due to the complex inter-class and intra-class dependencies. It can be challenging to distinguish between normal and cancerous cells because the underlying tissue structures often merged and have similar morphological structures. This convolution of structural features contributes additional complexities that hinder accurate evaluation and identification. To address these drawbacks proposed an Artificial Recurrent Neural Network with Levenberg-Marquardt Method (ARNN-LMM) based elapid encryption to improve security and predict colon diseases using IoT-enabled devices. Initially, Colon Cancer Histopathological Images are collected to serve as the input image. In order to reduce the disturbances in the background, the pre-processing of the raw images is done first using a pixel-wise thresholding (PWT) method, which is used to provide the image with a better appearance in addition to reduction of the noise. A wavelet domain transformer (WavEnhancer) is then used to enhance clarity at the pixel level and hence effectively enhance the overall image quality. Circular Mesh Network (CirMNet) is a shape based feature extraction technique, which extracts structural, statistical, and property-based features of image. The refined features are fed into a classification employing ARNN-LMM to detect colon abnormality. The trained net model is encrypted using elapid encryption and stored in cloud for ensuring secure access. In Disease Prediction Phase, an Internet of Things (IoT) device captures patient data and transmitted to the cloud, where the model is decrypted and analyze the features to predict the patient has disease or non-disease by a trained model. An suggested model attains 97.45%, 2.55% and 97.44% accuracy, FPR and specificity in detecting the colon disease. Similarly the model enhances colon disease detection effectively capturing statistical features along with enhancing the security of the trained model with cryptography algorithm.
Tripathi et al. (Sun,) studied this question.