High‐Performance Computing‐Based Brain Tumor Detection Using Parallel Quantum Dilated Convolutional Neural Network

In the healthcare field, brain tumor causes irregular development of cells in the brain. One of the popular ways to identify the brain tumor and its progression is magnetic resonance imaging (MRI). However, existing methods often suffer from high computational complexity, noise interference, and limited accuracy, which affect the early diagnosis of brain tumor. For resolving such issues, a high-performance computing model, such as big data-based detection, is utilized. As a result, this work proposes a novel approach named parallel quantum dilated convolutional neural network (PQDCNN)-based brain tumor detection using the Map-Reducer. The data partitioning is the prime process, which is done using the Fuzzy local information C-means clustering (FLICM). The partitioned data is subjected to the map reducer. In the mapper, the Medav filtering removes the noise, and the tumor area segmentation is done by a transformer model named TransBTSV2. After segmenting the tumor part, image augmentation and feature extraction are done. In the reducer phase, the brain tumor is detected using the proposed PQDCNN. Furthermore, the efficiency of PQDCNN is validated using the accuracy, sensitivity, and specificity metrics, and the ideal values of 91.52%, 91.69%, and 92.26% are achieved.