Explainable Deep Learning Model With the Internet of Medical Devices for Early Lung Abnormality Detection

Abstract

The decision-making process in healthcare monitoring systems has extensively used deep learning and the Internet of Things (IoT). Among the new uses in today's procedures is disease prediction. A difficult challenge in computer-aided diagnosis (CAD) is lung cancer prediction, which is addressed in this paper's solution using deep learning and IoT. IoT medical devices send disease-related data to the server, as lung cancer is a hazardous medical condition that must be detected faster. Following processing, a multi-layer Convolutional Neural Network (ML-CNN) model is used to classify the medical data into benign and malignant groups. Enhanced Particle swarm optimization (EPSO) is also used to enhance learning capacity (accuracy and loss). Medical data from the Internet of Medical Things (IoMT), including sensor data and Computed Tomography (CT) scan results, is used in this stage. The information from sensors and IoMT devices' picture data is collected for this purpose, and classification operations are then performed. The suggested method's accuracy, precision, sensitivity, specificity, F-score, and computation time are compared to well-known current approaches such as the Support Vector Machine (SVM), Probabilistic Neural Network (PNN), and Convolutional Neural Network (CNN). Linear Imaging and Self-Scanning Sensor (LISS) and Lung Image Database Consortium (LIDC) datasets were the two lung datasets used for this performance evaluation. Trial results indicate that the recommended strategy may aid in the timely and accurate identification of lung cancer in radiologists compared to other techniques. The efficacy of the suggested ML-CNN was examined through Python analysis. The results showed that the accuracy was superior to the number of instances, the precision was superior to the number of cases, and the sensitivity was superior to several instances, the F-score was superior to the number of cases, the error rate was inferior to the number of cases, and the computation time was inferior to the total number of instances computed for the proposed work, even when accounting for prior knowledge. Previous efforts are outperformed by this method, as shown by the suggested ML-CNN architecture.