Owing to the rapid growth of medical imaging technologies, medical image analysis is entered the period of big data for proper diagnosis of diseases. Intracranial hemorrhage (ICH) means a pathological disorder that requires quick decision making and diagnosis. Computed tomography (CT) can be accurate and extremely dependable diagnosis method for detecting hemorrhages. Automatic recognition of ICH from CT scans by using a computer-aided diagnosis (CAD) method was helpful in detecting and classifying various grades of ICH. Due to the recent development of deep learning (DL) methods in image processing applications, numerous medical imaging approaches use it. This article introduces innovative Big Data Analytics with Deep Learning based Automated Brain Intracranial Haemorrhage Diagnosis and Classification Model (BDDL-IHDCM). The presented BDDL-IHDCM model examines the computed tomography (CT) scans for detecting and classifying ICH. To accomplish this, the presented BDDL-IHDCM model applies bilateral filtering (BF) technique for noise elimination procedure. The BDDL-IHDCM method employs neural architectural search network (NASNet) feature extractor with Bayesian optimization (BO) based hyperparameter tuning. For ICH detection and classification, a grey wolf optimizer (GWO) with auto encoder (AE) model is utilized in this study. To handle big data, Hadoop MapReduce will be used. To exhibit the significant performance of the presented BDDL-IHDCM method, a comprehensive simulation analysis is made by making use of benchmark dataset. The experimental outcomes indicate the betterment of the presented BDDL-IHDCM method to other DL models with maximum accuracy of 96.22%.

Deep learning strategies have been achieved over the historical decades to resolve many computer vision applications. Recently, these deep learning algorithms have been extensively used as a tool in classification problems. Generally, the deep learning algorithms trained with gradient-based optimizers, which has some downsides such as the slow speed of convergence and stuck in local minima. As a solution, the planned work using meta-heuristic based Grey Wolf and Whales optimization algorithms for the automatic plant disease detection model. The planned work has explored the application of automatic plant disease identification through the leaf images with the help of the image processing approach. The planned research has evaluated the deep learning algorithm with Grey Wolf and Whales optimization techniques using the three types of datasets, such as Plant Village, New Plant Disease, and Rice Leaf Disease databases. The simulation consequences illustrate that the computational efficiency of the Grey Wolf and Whales based automatic disease identification process is boosted when coupled with the deep learning method.

Agriculture is the chief food division in India. Mainly most of the peoples are using the rice for food, even in world wide. But the diseases of rice plant threatening the food part. The appropriate recognition and initial prevention of rice leaf diseases are important for cultivating production. Novel method of image recognition with deep learning algorithm will prevent this disaster. The paper presents a deep model architecture to notice such diseases through the leaf of the rice. The model that has formed is trained to analyse diseased leaf and thereafter identify the type of disease from the rice leaf. The paper is planned to use InceptionV3 with transfer learning concept. The outcomes of this paper show that the transfer learning concept for rice leaf disease image recognition presents a speedy and reasonable detection. The accuracy outcomes in the identification of diseases exposed that the presented model is capable and can greatly influence the efficient identification of the diseases.