Big Data Analytics with Optimal Deep Learning Model for Medical Image Classification

In recent years, huge volumes of healthcare data are getting generated in various forms. The advancements made medical imaging tremendous owing to which biomedical image acquisition has become easier and quicker. Due such massive generation big data, the utilization new methods based on Big Data Analytics (BDA), Machine Learning (ML), Artificial Intelligence (AI) have essential. this aspect, current research work develops a with Cat Swarm Optimization deep (BDA-CSODL) technique for classification Apache Spark environment. aim proposed BDA-CSODL is classify images diagnose disease accurately. involves different stages operations as preprocessing, segmentation, feature extraction, classification. addition, also follows multi-level thresholding-based segmentation approach detection infected regions image. Moreover, convolutional neural network-based Inception v3 method utilized study extractor. Stochastic Gradient Descent (SGD) model used parameter tuning process. Furthermore, CSO Long Short-Term Memory (CSO-LSTM) employed determine appropriate class labels it. Both SGD design approaches help improving overall performance technique. A wide range simulations was conducted benchmark datasets comprehensive comparative results demonstrate supremacy under measures.